Problem Set 4 1. The following data shows average exam scores of sample students in three different schools. School A School B School C 98 75 72 34 68 100 88 97 76 85 76 77 85 93 99 59 61 55 75 86 93...

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Problem Set 4 1. The following data shows average exam scores of sample students in three different schools. School A School B School C 98 75 72 34 68 100 88 97 76 85 76 77 85 93 99 59 61 55 75 86 93 97 47 91 88 79 69 100 78 65 a. Suppose you are conducting one-way analysis of variance. What would be the primary goal of doing this? Answer this using at least 50 words. b. Estimate analysis of variance (ANOVA) using SPSS and provide the results. You can simply copy and paste the results from SPSS. c. Interpret the results using at least 50 words. 2. The follow table shows monthly temperatures (in Fahrenheit) recorded in four U.S. states. Month California Arizona Washington Pennsylvania January February March April May June July August September October November December 65 70 78 85 90 93 95 98 95 85 73 68 60 75 82 88 93 97 101 103 104 98 90 84 32 35 40 46 57 67 75 77 71 65 56 48 15 22 35 45 57 68 72 70 68 60 48 30 a. Suppose you are conducting one-way analysis of variance. State null and alternative hypothesis test. Answer this using at least 50 words. b. Estimate analysis of variance (ANOVA) using SPSS and provide the results. You can simply copy and paste the results from SPSS. c. Interpret the results using at least 50 words. Problem Set 5 1. Suppose that the following data shows heights, weights and annual income of sampled residents in the County of Riverside. Height(in cm) Weight (in kg) Income (in $) 198 175 172 134 168 145 188 157 176 195 90 70 71 55 60 59 89 55 80 86 75,000 56,000 90,000 89,000 101,000 34,000 55,000 29,000 98,000 79,000 a. Suppose you are estimating a simple linear regression model using the above data. Estimate two separate simple linear regression equations showing the following relationships, using SPSS: - The impact of height on weight - The impact of height on annual income - The impact of income on height b. Is relationship between two variables statistically significant in each regression model? Explain this using at least 100 words. c. Interpret the slope of a statistically significant independent variable(s) using at least 100 words. 2. Please refer to the database “DATA_HOUSE.xlsx”. The database shows 30 residential house transactions that occurred, in Chester County, Pennsylvania between 2002 and 2005. The table below shows description of each variable. Variable Description Price Resarea Bsmtarea Age Comm400 Ind400 Recland400 House Sale Value in dollar Square footage of total residential space Square footage of basement Age of house at time of sale Proportion of land within 400M of the house in commercial use Proportion of land within 400M of the house in industrial use Proportion of land within 400M of the house in recreational use a. Suppose you are estimating a multiple linear regression model using the above data, using SPSS. Estimate a multiple linear regression model that shows the impact of all housing characteristics on the price of house. Copy and paste the results into a word document. b. Identify statistically significant variables from this regression model, and justify your choice, using at least 100 words. c. Interpret the slope of a statistically significant independent variable(s) using at least 100 words.
Answered Same DayAug 30, 2021

Answer To: Problem Set 4 1. The following data shows average exam scores of sample students in three different...

Vignesh answered on Aug 31 2021
139 Votes
Problem Set 5
1. Suppose that the following data shows heights, weights and annual income of sampled residents in the County of Riverside.
    Height(in cm)
    Weight (in kg)
    Income
(in $)
    198
175
172
134
168
145
188
157
176
195
    90
70
71
55
60
59
89
55
80
86
    75,000
56,000
90,000
89,000
101,000
34,000
55,000
29,000
98,000
79,000
a. Suppose you are estimating a simple linear regression model using the above data. Estimate two separate simple linear regression equations showing the following relationships, using SPSS:
- The impact of height on weight
Table 1
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    95.0% Confidence Interval for B
    
    B
    Std. Error
    Beta
    
    
    Lower Bound
    Upper Bound
    1
    (Constant)
    -33.206
    17.066
    
    -1.946
    .088
    -72.560
    6.148
    
    Height
    .613
    .099
    .909
    6.176
    .000
    .384
    .842
    a. Dependent Variable: Weight
(i) From above table, the fitted simple linear regression model is
Weight = -33.206 + 0.613 height
- The impact of height on annual income
Table 2
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    95.0% Confidence Interval for B
    
    B
    Std. Error
    Beta
    
    
    Lower Bound
    Upper Bound
    1
    (Constant)
    30370.393
    74209.186
    
    .409
    .693
    -140756.296
    201497.083
    
    Height
    235.536
    431.615
    .189
    .546
    .600
    -759.770
    1230.843
    a. Dependent Variable: Income
(ii) From above table, the fitted simple linear regression model is
Income = 30370.393 + 235.536 height
- The impact of income on height
Table 3
    Coefficientsa
    Model
    Unstandardized Coefficients
    Standardized Coefficients
    t
    Sig.
    95.0% Confidence Interval for B
    
    B
    Std. Error
    Beta
    
    
    Lower Bound
    Upper...
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