Delete this page before submission Delete this page before submission MATH 1071 – Research Methods and Statistics Case Study: Research and Analysis – Part 2 DUE: 6:00PM Friday, 25th September This...

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Delete this page before submission Delete this page before submission MATH 1071 – Research Methods and Statistics Case Study: Research and Analysis – Part 2 DUE: 6:00PM Friday, 25th September This Case Study must be completed using this template. You may change the visual style including font, colour, figures, tables, etc., however do not move the report body sections around – answer them in the order they are given to you. Submission Instructions · Submission is via Learn Online. There will be a link on the main page of the course website. If you have problems submitting, keep proof of your submission attempt and contact us immediately at: [email protected]. I’ll sort it out! · Once submitted, do not edit any electronic files of your work. Case Study Instructions · This Case Study covers weeks 2-5 of your lectures. Part 1 Mark Report Body 50 Appendix 1 20 Appendix 2 14 Appendix 3 16 Presentation 10 TOTAL 110 · Late submissions, without an extension being granted, will attract a penalty of 10% per day or any part thereof beyond the due date and time. Please refer to the Course Outline for the course policy regarding extensions. · Not deleting the pages/green boxes as requested will result in a loss of presentation marks. · Your submission should be typed and observe the page limits. Delete this page before submission Delete this page before submissionPlagiarism Plagiarism is a specific form of academic misconduct. We encourage and support working in groups and seeking help, however your final submission must always represent your individual work. If plagiarism is found, all parties involved will be penalised. You need to retain all your assignment computer files (Excel, JASP, Word etc.), which must remain unchanged after submission, for the purposes of checking, if required. Plagiarism covers, but is not limited to, the following actions: · Direct copying of the work of other persons, from one or more source, without clearly indicating the origin; · Sending your files to another student for any reason whatsoever, even for the purposes of checking or comparing work; · Submitting another student’s work in whole or in part; · Submitting work that has been written by someone else on the student’s behalf; · Copying computer files without clearly indicating their origin; · Submitting work that has been derived, in whole or in part, from another student’s work by a process of mechanical transformation (e.g. changing variable names in computer files, paraphrasing); · A group-based effort to produce one assignment shared between individuals. Working together is encouraged, however each written assignment submitted by each student must be written in its entirety by the individual student, including running analyses, producing Excel and/or JASP output. Working together where one individual types the collective thoughts of a group/produces analyses and/or spread sheets and then shares these files will be investigated for plagiarism. All parties found to be involved in academic misconduct will incur, if appropriate, a penalty and a record in the University's Academic Integrity Database (UniSA Assessment and Procedures Manual 2020, Section 9, Clause 9.2.3 i.). If you are unsure about what constitutes Academic Integrity, you are always welcome to ask us for more information before submitting. Delete this page before submission Delete this page before submission Case Study Active transport interventions: understanding patterns of bike share use As a health promotion officer for the state government you have been tasked with developing city-wide interventions to promote and encourage active travel among adult South Australians to improve local health. You have reviewed existing research and have decided to focus on urban initiatives to increase the uptake of bike shares around the metropolitan area to encourage increased levels of physical activity. As a first step in your investigations, you need to understand existing patterns of bike share use in metropolitan centres. In particular, you want to know whether bike share demand differs between weekends and weekdays, e.g. for people in transit to work vs recreational use on weekends. You are also interested to learn whether there is a seasonal effect on the frequency with which bike shares are accessed to inform the structure and timing of your developed activity interventions. As part of your intervention, you are also considering providing the option of bike share user registration so that you can send health resource information and promotions to encourage continued bike share use and active lifestyles in general. You want to investigate whether patterns of bike share use differ between casual and registered users and the extent to which outdoor temperature impacts bike share use. To answer these questions, you have sourced and downloaded bike share data from two major metropolitan areas, namely Montreal, Canada, and Washington, D.C., USA. While climates differ between these cities and your local community, this data can be used to gain insight into typical drivers of bike share use to inform city-wide active transport interventions. You’ve done your background research and now you’re ready to jump into the statistical analysis – Excel and JASP are waiting for you. However, since the report is designed to inform the local government and community, whatever you write and present has to be in non-technical terms – so make sure your report is in everyday language free of statistical jargon. Research Question and AnalysisThis assignment covers Part 2 of the course, using bike share data you have obtained from online data repositories. The data here are used as guidelines for what can be expected on the road ahead as you consider city-wide cycling interventions to promote active transport. Understanding patterns and trends in bike share use is fundamental to the intervention development, so get ready to do your job! UNIVERSITY OF SOUTH AUSTRALIA Assignment Cover Sheet – Internal An Assignment cover sheet needs to be included with each assignment. Please complete all details clearly. When submitting the assignment online, please ensure this cover sheet is included at the start of your document. (Not as a separate attachment.) Please check your Course Information Booklet or contact your School Office for assignment submission locations. Name: Student ID                 Email: Course code and title: MATH 1071 – Research Methods and Statistics School: ITMS/HLS Program Code: Course Coordinator: Dr Belinda Chiera Tutor: Day, Time, Location of Tutorial: Assignment number: 2 (Research Question and Analysis – Part 2) Due date: by 6:00PM on Friday, 25th September Assignment topic as stated in Course Outline: Case Study Report Further Information: (e.g. state if extension was granted and attach evidence of approval, Revised Submission Date)   I declare that the work contained in this assignment is my own, except where acknowledgement of sources is made. I authorise the University to test any work submitted by me, using text comparison software, for instances of plagiarism. I understand this will involve the University or its contractor copying my work and storing it on a database to be used in future to test work submitted by others. I understand that I can obtain further information on this matter at http://www.unisanet.unisa.edu.au/learningconnection/student/studying/integrity.asp Note: The attachment of this statement on any electronically submitted assignments will be deemed to have the same authority as a signed statement. Signed: Date: Date received from student Assessment/grade Assessed by: Recorded: Dispatched (if applicable): [ENTER RESEARCH QUESTION AS THE TITLE] [PART 2] [Enter the date of submission] prepared by [Enter your name here] Student ID: [Enter your student ID] Tutorial: [Enter day, time, location of your tutorial] Tutor: [Enter your tutor's name] Delete this page before submission The report length needs to be as follows: A maximum of 3 pages of report writing excluding the coversheet and infographic. If for any reason, you include figures and/or tables, we will look at the equivalent of 3 pages of writing (i.e. figures and tables don’t need to fit inside the three page limit!). The 3 page limit includes the introduction and conclusion. We will not mark more than 3 pages of writing (excluding the coversheet, infographic, and any figures and tables), so ensure you stay within these limits. Font Type: up to you, but keep in mind it should be easy to read – make it easy for us to give you marks! Font Size: minimum 11pt (it shouldn’t look smaller than this!) Line Spacing: minimum single line spacing Any text or font size smaller than this sentence will incur 0 marks. (this is too small! Don’t make your text this small!!! ) Suggested Assignment Work Timeline: If you’re not sure how to start the assignment, a suggested work breakdown schedule is below. Feel free to use it (or ignore it) as you like Also, some people like to complete each appendix and then write it up in the report, others prefer to complete all appendices and then write up the report in one go. Do whichever suits you! The weeks below indicate which week the lecture covers the material. Relevant exercises in the following week’s tutorials will be indicated in each set of tutorial questions. The report can be written at any time (we don’t cover this in lectures!). · Weeks 2 & 3: Appendix 1. · Week 4: Appendix 2. · Week 5: Appendix 3. · Anytime Report body write-up and SUBMIT! Yay! Delete this page before submission By Season and Day of the Week … … … Bike Share UsersCasual shares by temperature Delete the green boxes before submission (4 marks) Introduction Write up to two paragraphs for this part. Ensure you include: · A reminder of what you are working to achieve through the analysis · The aims of this report in terms of the types of questions you will investigate Between 6-8 lines is OK for each paragraph. (10 marks) Understanding patterns of bike share use This section summarises the analysis in Appendix 1 Explain the aim of this section.
Answered Same DaySep 22, 2021

Answer To: Delete this page before submission Delete this page before submission MATH 1071 – Research Methods...

Payal answered on Sep 24 2021
135 Votes
Final Solution & Analsysis output/66021_Final Solution.docx
        
        UNIVERSITY OF SOUTH AUSTRALIA    
Assignment Cover Sheet – Internal
An Assignment cover sheet needs to be included with each assignment. Please complete all details clearly.
When submitting the assignment online, please ensure this cover sheet is included at the start of your document. (Not as a separate attachment.)
Please check your Course Information Booklet or contact your School Office for assignment submission locations.
        Name:
        Student ID
         
         
         
         
         
         
         
         
        
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        Course code and title: MATH 1071 – Research Methods and Statistics
        School: ITMS/HLS
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        Course Coordinator: Dr Belinda Chiera
        Tutor:
        Day, Time, Location of Tutorial:
        Assignment number: 2 (Research Question and Analysis – Part 2)
        Due date: by 6:00PM on Friday, 25th September
        Assignment topic as stated in Course Outline: Case Study Report
Further Information: (e.g. state if extension was granted and attach evidence of approval, Revised Submission Date)
         
I declare that the work contained in this assignment is my own, except where acknowledgement of sources is made.
I authorise the University to test any work submitted by me, using text comparison software, for instances of plagiarism. I understand this will involve the University or its contractor copying my work and storing it on a database to be used in future to test work submitted by others.
I understand that I can obtain further information on this matter at http://www.unisanet.unisa.edu.au/learningconnection/student/studying/integrity.asp
Note: The attachment of this statement on any electronically submitted assignments will be deemed to have the same authority as a signed statement.
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[ENTER RESEARCH QUESTION AS THE TITLE]
[PART 2]
[Enter the date of submission]
prepared by
[Enter your name here]
Student ID: [Enter your student ID]
Tutorial: [Enter day, time, location of your tutorial]
Tutor: [Enter your tutor's name]


By Season and Day of the Week
… … …
    Bike Share Users    Casual shares by temperature
INTRODUCTION
The aim of this activity is to do analysis of Bike shares data so as to enable us understand how the frequency of bike shares differs from temperature and the time of the day, so as to promote Bike shares by the relevant policies intervention.
The above Bike share data will be analyzed from different angels, like what is the count of registered and Casual bike shares, and how they differ across weekdays and the weekends for different temperature.
ANALYSIS APPENDIX 1
Aim -
The aim of this analysis is to explore the Total Bike Share usage count by the week day and weekends across different seasons of the year. This will help us to gain insight in which season there is more usage.
a) Bike Share on Weekend & Weekday for different Season
It can be seen from the above analysis that the Usage count is more on weekends for every season of the year. This means that people are using bike more for a recreational purpose rather than for their commuting purposes.
Percentage of Bike Share occur in Summer & on Weekend 73.4% out of total in Summer
Percentage of Bike Share occur in Spring & on Weekday 24.7% out of total in Spring
b) Probability for different combinations –
(i) The probability of a bike share occurring in winter 0.17
(ii)The probability of a bike share occurring in summer and on weekend 0.24
(iii)The probability of a bike share occurring on a weekday given the season is spring 0.06
(iv)Whether the number of bike shares on a weekday is statistically independent of season No
The above table shows the probability for various seasons and the week of the day. It can be seen from the above table values that the probability of Bike sharing is higher on the weekends rather than on the weekdays.
Moreover, as the probabilities on weekdays are almost the same for different season, therefore we cannot say that bike usage is related with the different seasons. In such case, it is not recommended to target a particular season for weekday active transport interventions.
c) 100% stacked Column Chart
The two observations we can draw from the above Stacked bar chart is –
· Demand for Bike sharing is moving in equal ratio throughout the season
· Almost 80% of the bike share is account by the weekends
ANALYSIS APPENDIX 2
Aim –
The aim of this study is to explore the bike share usage for different users (Casual & Registered) across different seasons of the year & further finding out if there is any specific season where the bike share is surging
a) Central tendency and dispersion for daily bike share demand in summer for casual and registered bike share users
The above table shows the Descriptive Statistics for the Casual Bike Share Users across the different seasons of the Year. Now we can see from the above table that the Mean and median values highly differ across different Seasons, therefore, it would be recommended that the Median is considers as Central Measure of Tendency. On a whole Both Median and Mean tells about the Average number of Bike users for the Respective seasons, but median is more suitable as it excludes the Outliers or the very high or low values in the dataset.
The boxplot is a distribution of the values in the data. It can be seen from the above figures that for the casual users, the values are constantly changing with very high values during summers, while for registered users, there is clear pattern in the bike usage.
b) Central tendency and dispersion for daily bike share use for in clear and rainy weather conditions
It can be seen from the above table that Both Mean and the Median are almost the same for the different weather conditions, therefore any of them can be used for interpreting the results. Moreover, from the above result it can be concluded that on an average there is less demand for bikes on a rainy day.
c) investigation into the number of casual and registered users
Summarization
As we can see that during rainy season, there is very less usage of Bike, so active interventions for bike share across all season would not be a feasible idea. During the rainy season, despite of any active policy, people would not be preferring bikes
ANALYSIS APPENDIX 3
Aim –
In this section, the aim is to build a linear regression model taking the casual & registered users as a dependent variable in each case & temperature as an independent variable & see how the temperature is affecting the casual & registered bike share usage
a) Correlation between temperature & Bike share
Linearity of Scatterplot
It can be seen from the above scatterplot that the line is straight with a upward sloping direction, therefore we can conclude that we a direct positive relationship between the temperature and Casual and Registered users.
Data- Data is fairly normal with no issues, as can be seen from the Bell-shaped curve.
b) Regression Charts
Regression for Casual Users
R2- It tells us how well our model is predicting the data. In the above case R2 is 0.316 which 31%. Therefore, it can be concluded that our built model explains only about 31% of the variation present in the data.
Regression for Registered Users
R2- It tells us how well our model is predicting the data. In the above case R2 is 0.257 which 25% Therefore it can be concluded that our built model explains only about 25% of the variation present in the data. Therefore, overall, it is not a good model in predicting the Bike users based on the temperature.
c) Predicted number of casual and registered users
Number of Casual User when the Temperature is 28 is about (-8828). This means as the intercept for temperature is negative, therefore when temperature is 28, there would decline in Casual Bike Users by approx. 8828 counts.
Number of Registered User when the Temperature is 28 is about 42,661 This means as the intercept for temperature is positive, therefore when temperature is 28, there would increase in Registered Bike Users by approx. 42,661 counts
d) Scatterplot of casual bike shares versus temperature
It can be seen from the above scatterplot that as the temperature increases the Casual bikers also increases, therefore having a direct positive relationship between them.
CONCLUSION & RECOMMENDATION
As from the above analysis, it has been observed, that Bike share usage is very less during the rainy season as compared to other season so there would be no point in making extra efforts & sources to boost bike share usage during rainy season. It would be feasible to concentrate on the other season
Moreover, it is also observed that the Bike share usage was comparatively higher during the weekends across all seasons. This means that people are using bikes as a recreational purpose rather than for day to day commutation. This might be due to less integrated infrastructure system of the city
Casual    18.86    18.86    10.66    12.3    12.3    11.48    9.02    8.1999999999999993    9.02    8.1999999999999993    8.1999999999999993    9.02    9.84    9.84    13.94    11.48    8.1999999999999993    9.02    16.399999999999999    9.84    15.58    9.02    12.3    12.3    13.94    15.58    11.48    8.1999999999999993    8.1999999999999993    13.12    13.94    17.22    24.6    13.94    18.86    24.6    27.06    19.68    13.94    22.14    10.
66    14.76    19.68    18.86    13.94    15.58    14.76    18.04    14.76    18.86    19.68    16.399999999999999    15.58    18.04    21.32    28.7    24.6    14.76    16.399999999999999    18.86    31.16    25.42    22.14    24.6    16.399999999999999    15.58    21.32    30.34    25.42    18.86    24.6    22.14    21.32    22.96    26.24    22.96    20.5    27.06    29.52    21.32    23.78    24.6    24.6    24.6    26.24    27.06    27.06    26.24    21.32    22.96    27.06    29.52    25.42    24.6    24.6    36.9    32.799999999999997    29.52    30.34    30.34    32.799999999999997    33.619999999999997    37.72    38.54    35.26    33.619999999999997    33.619999999999997    28.7    27.88    31.16    29.52    31.98    33.619999999999997    31.16    33.619999999999997    36.08    34.44    33.619999999999997    33.619999999999997    31.98    35.26    32.799999999999997    34.44    34.44    35.26    36.9    34.44    31.16    30.34    31.16    33.619999999999997    34.44    36.9    37.72    36.9    32.799999999999997    32.799999999999997    32.799999999999997    34.44    36.9    35.26    35.26    35.26    32.799999999999997    33.619999999999997    30.34    31.16    31.98    32.799999999999997    33.619999999999997    33.619999999999997    32.799999999999997    30.34    28.7    29.52    32.799999999999997    30.34    22.14    26.24    28.7    30.34    30.34    30.34    30.34    30.34    31.98    27.88    22.14    22.14    23.78    24.6    20.5    16.399999999999999    17.22    23.78    26.24    23.78    27.06    27.06    27.88    30.34    24.6    22.96    27.06    25.42    25.42    25.42    23.78    26.24    24.6    20.5    20.5    21.32    19.68    17.22    19.68    22.96    23.78    21.32    18.86    15.58    21.32    22.14    26.24    22.96    19.68    17.22    13.94    17.22    16.399999999999999    17.22    15.58    17.22    20.5    20.5    19.68    13.12    15.58    13.94    12.3    13.12    16.399999999999999    15.58    21.32    20.5    12.3    13.12    16.399999999999999    19.68    16.399999999999999    8.1999999999999993    8.1999999999999993    14.76    21.32    23.78    16.399999999999999    10.66    17.22    13.94    18.86    16.399999999999999    9.84    9.02    12.3    18.86    18.86    10.66    25.42    18.86    16.399999999999999    13.94    13.12    17.22    18.04    12.3    13.94    13.94    12.3    8.1999999999999993    14.76    17.22    17.22    13.94    17.22    19.68    13.94    24.6    18.04    20.5    16.399999999999999    12.3    14.76    22.96    26.24    23.78    14.76    21.32    25.42    29.52    29.52    29.52    19.68    26.24    22.96    27.06    21.32    20.5    25.42    27.06    21.32    21.32    22.14    26.24    23.78    23.78    18.04    20.5    22.96    26.24    29.52    32.799999999999997    27.88    22.14    25.42    30.34    26.24    29.52    31.16    30.34    25.42    25.42    26.24    27.06    24.6    26.24    28.7    29.52    25.42    28.7    29.52    27.06    27.06    30.34    30.34    26.24    28.7    27.06    24.6    26.24    30.34    31.16    34.44    34.44    33.619999999999997    28.7    29.52    30.34    30.34    30.34    27.06    25.42    32.799999999999997    37.72    35.26    36.9    37.72    37.72    37.72    41    39.36    31.98    33.619999999999997    32.799999999999997    32.799999999999997    32.799999999999997    32.799999999999997    36.9    36.08    38.54    38.54    35.26    33.619999999999997    35.26    36.08    36.9    36.9    34.44    33.619999999999997    34.44    36.08    32.799999999999997    32.799999999999997    32.799999999999997    33.619999999999997    33.619999999999997    31.98    33.619999999999997    35.26    31.16    27.88    35.26    31.16    31.16    32.799999999999997    33.619999999999997    30.34    32.799999999999997    33.619999999999997    29.52    27.06    28.7    29.52    29.52    29.52    27.88    27.06    26.24    28.7    25.42    25.42    25.42    29.52    29.52    30.34    28.7    18.04    17.22    22.14    24.6    21.32    22.96    20.5    27.06    24.6    22.14    23.78    25.42    27.06    16.399999999999999    17.22    14.76    16.399999999999999    15.58    14.76    13.12    18.04    18.86    22.14    24.6    24.6    21.32    14.76    15.58    17.22    17.22    17.22    18.04    16.399999999999999    18.04    24.6    24.6    21.32    13.12    15.58    17.22    16.399999999999999    21.32    18.86    13.94    14.76    16.399999999999999    17.22    16.399999999999999    17.22    20.5    17.22    331    131    120    108    82    88    148    68    54    41    43    25    38    54    222    251    117    9    78    47    72    61    88    100    354    120    64    53    47    149    288    397    208    140    218    259    579    532    137    231    123    214    640    114    244    316    191    46    247    724    982    359    289    321    424    884    1424    307    898    1651    734    167    413    571    172    879    1188    855    257    209    529    642    121    1558    669    409    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weekday    
Autumn    Spring    Summer    Winter    0.24553772379146857    0.2476143543620139    0.26510764173167833    0.19597819583466966    weekend    
Autumn    Spring    Summer    Winter    0.75446227620853146    0.75238564563798604    0.73489235826832167    0.80402180416533031    
i
14
SeasonweekendweekdayGrand Total
Autumn18370075978492434856
Spring18229775999522422929
Summer23427568451343187890
Winter13612663318051693071
Grand Total736400623747409738746
SeasonweekdayweekendGrand Total
Autumn0.060.190.25
Spring0.060.190.25
Summer0.090.240.33
Winter0.030.140.17
Grand Total0.240.761.00
User TypeTotal Bike shares
Casual392135
Registerd1693341
Final Solution & Analsysis output/Appendix 2a.jasp
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data.bin
index.html
Results
Descriptive Statistics
                
                    
                        
                            
                            Descriptive Statistics
                            
                            
                            
                        
                    
                
                        
                Casual
                
            
                 
                            
                Autumn
                            
                Spring
                            
                Summer
                            
                Winter
                
            
                Valid
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                            
            
                Missing
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                            
            
                Mean
                                        
                1251.912
                                        
                364.956
                                        
                1137.474
                                        
                685.439
                            
            
                Median
                                        
                1067.500
                                        
                217.500
                                        
                898.000
                                        
                485.000
                            
            
                Mode
                            
                ᵃ
                            
                799.000
                                        
                47.000
                                        
                653.000
                                        
                178.000
                            
            
                Std. Deviation
                                        
                634.556
                                        
                453.693
                                        
                730.354
                                        
                560.684
                            
            
                Skewness
                                        
                0.975
                                        
                3.254
                                        
                1.228
                                        
                1.867
                            
            
                Std. Error of Skewness
                                        
                0.226
                                        
                0.226
                                        
                0.226
                                        
                0.226
                            
            
                Kurtosis
                                        
                0.532
                                        
                14.340
                                        
                0.909
                                        
                3.687
                            
            
                Std. Error of Kurtosis
                                        
                0.449
                                        
                0.449
                                        
                0.449
                                        
                0.449
                            
            
                Minimum
                                        
                118.000
                                        
                9.000
                                        
                121.000
                                        
                50.000
                            
            
                Maximum
                                        
                3160.000
                                        
                3155.000
                                        
                3410.000
                                        
                3031.000
                            
            
                25th percentile
                                        
                799.000
                                        
                109.000
                                        
                663.250
                                        
                321.500
                            
            
                50th percentile
                                        
                1067.500
                                        
                217.500
                                        
                898.000
                                        
                485.000
                            
            
                75th percentile
                                        
                1498.750
                                        
                393.750
                                        
                1482.750
                                        
                891.750
                            
            
            
                ᵃ More than one mode exists, only the first is reported
                
Descriptive Statistics
                
                    
                        
                            
                            Descriptive Statistics
                            
                            
                            
                        
                    
                
            
                    
                            
                    
                    Casual
                    
                            
                    
                    Registered
                    
                
            
                 
                            
                Autumn
                            
                Spring
                            
                Summer
                            
                Winter
                            
                Autumn
                            
                Spring
                            
                Summer
                            
                Winter
                
            
                Valid
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                                        
                114
                            
            
                Missing
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                                        
                0
                            
            
                Mean
                                        
                1251.912
                                        
                364.956
                                        
                1137.474
                                        
                685.439
                                        
                4367.930
                                        
                2376.254
                                        
                4022.895
                                        
                4086.789
                            
            
                Std. Deviation
                                        
                634.556
                                        
                453.693
                                        
                730.354
                                        
                560.684
                                        
                1293.748
                                        
                1210.882
                                        
                1339.207
                                        
                1259.515
                            
            
                Minimum
                                        
                118.000
                                        
                9.000
                                        
                121.000
                                        
                50.000
                                        
                1689.000
                                        
                491.000
                                        
                674.000
                                        
                655.000
                            
            
                Maximum
                                        
                3160.000
                                        
                3155.000
                                        
                3410.000
                                        
                3031.000
                                        
                6820.000
                                        
                5315.000
                                        
                6456.000
                                        
                6911.000
                            
            
Boxplots
Casual
Registered
    
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Results
Descriptive Statistics
                
                    
                        
                            
                            Descriptive Statistics
                            
                            
                            
                        
                    
                
                        
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                4181.900
                                        
                1923.875
                            
            
                Median
                                        
                4803.000
                                        
                4172.500
                                        
                2079.000
                            
            
                Mode
                            
                ᵃ
                            
                3974.000
                                        
                3351.000
                                        
                795.000
                            
            
                Std. Deviation
                                        
                1838.976
                                        
                1748.902
                                        
                678.745
                            
            
                Minimum
                                        
                822.000
                                        
                605.000
                                        
                795.000
                            
            
                Maximum
                                        
                8714.000
                                        
                7836.000
                                        
                2918.000
                            
            
                25th percentile
                                        
                3606.000
                                        
                2943.500
                                        
                1614.000
                            
            
                50th percentile
                                        
                4803.000
                                        
                4172.500
                                        
                2079.000
                            
            
                75th percentile
                                        
                6293.000
                                        
                5350.500
                                        
                2418.000
                            
            
            
                ᵃ More than one mode exists, only the first is reported
                
Boxplots
Count
    
analyses.json
{
"analyses" : [
{
"id" : 0,
"module" : "Descriptives",
"name" : "Descriptives",
"options" : {
"addSmooth" : true,
"addSmoothCI" : true,
"addSmoothCIValue" : 0.950,
"chartType" : "_1noCharts",
"chartValues" : "_1frequencies",
"colorPalette" : "colorblind",
"descriptivesPiechart" : false,
"descriptivesQQPlot" : false,
"distPlotDensity" : false,
"frequencyTables" : true,
"graphTypeAbove" : "density",
"graphTypeRight" : "density",
"iqr" : false,
"kurtosis" : false,
"mad" : false,
"madrobust" : false,
"maximum" : true,
"mean" : true,
"median" : true,
"minimum" : true,
"mode" : true,
"percentileValuesEqualGroups" : false,
"percentileValuesEqualGroupsNo" : 4,
"percentileValuesPercentiles" : false,
"percentileValuesPercentilesPercentiles" : [],
"percentileValuesQuartiles" : true,
"plotCorrelationMatrix" : false,
"plotHeight" : 320,
"plotVariables" : false,
"plotWidth" : 480,
"range" : false,
"regressionType" : "smooth",
"scatterPlot" : false,
"shapiro" : false,
"showLegend" : true,
"skewness" : false,
"splitPlotBoxplot" : true,
"splitPlotColour" : false,
"splitPlotJitter" : false,
"splitPlotOutlierLabel" : false,
"splitPlotViolin" : false,
"splitPlots" : true,
"splitby" : "Weather",
"standardDeviation" : true,
"standardErrorMean" : false,
"statisticsValuesAreGroupMidpoints" : false,
"sum" : false,
"variables" : [ "Count" ],
"variance" : false
},
"progress" : null,
"results" : {
".meta" : [
{
"name" : "stats",
"type" : "table"
},
{
"meta" : [],
"name" : "tables",
"type" : "collection"
},
{
"meta" : [
{
"name" : "splitPlots_Count",
"type" : "image"
}
],
"name" : "splitPlots",
"type" : "collection"
}
],
"citation" : [ "JASP Team (2020). JASP (Version 0.13.1) [Computer software]." ],
"name" : "",
"splitPlots" : {
"collection" : {
"splitPlots_Count" : {
"aspectRatio" : 0.0,
"convertible" : true,
"data" : "resources/0/_0_t-1137794190.png",
"editOptions" : {
"xAxis" : {
"settings" : {
"labels" : [ "Clear", "Cloudy", "Rainy" ],
"shown" : [ "Clear", "Cloudy", "Rainy" ],
"title" : "Weather"
},
"type" : "ScaleDiscrete"
},
"yAxis" : {
"settings" : {
"breaks" : [ 2000, 4000, 6000, 8000 ],
"expand" : [ 0.050, 0, 0.050, 0 ],
"labels" : [ "2000", "4000", "6000", "8000" ],
"title" : "Count"
},
"type" : "ScaleContinuous"
}
},
"editable" : true,
"height" : 320,
"name" : "splitPlots_Count",
"revision" : 0,
"status" : "complete",
"title" : "Count",
"width" : 480
}
},
"initCollapsed" : false,
"name" : "splitPlots",
"title" : "Boxplots"
},
"stats" : {
"casesAcrossColumns" : true,
"data" : [
{
"Level" : "Clear",
"Maximum" : 8714.0,
"Mean" : 4816.501492537313,
"Median" : 4803.0,
"Minimum" : 822.0,
"Missing" : 0,
"Mode" : 3974.0,
"Std. Deviation" : 1838.975808215366,
"Valid" : 335,
"Variable" : "Count",
"q1" : 3606.0,
"q2" : 4803.0,
"q3" : 6293.0
},
{
"Level" : "Cloudy",
"Maximum" : 7836.0,
"Mean" : 4181.90,
"Median" : 4172.50,
"Minimum" : 605.0,
"Missing" : 0,
"Mode" : 3351.0,
"Std. Deviation" : 1748.902267542169,
"Valid" : 100,
"Variable" : "Count",
"q1" : 2943.50,
"q2" : 4172.50,
"q3" : 5350.50
},
{
"Level" : "Rainy",
...
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