Use the Health SPSS dataset to answer the following questions. Assume a .05 level of significance:1....

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Use the Health SPSS dataset to answer the following questions. Assume a .05 level of significance:1. Is there a statistically significant difference in weight between men and women? (Conduct an independentttest.)2. Identify the independent and dependent variables.3. Write a null hypothesis.4. Write an alternative non-directional (2-tail) hypothesis.5. Interpret your results. Guidelines for interpreting independentttests can be found inWhat to Include When Writing Up IndependenttTest Results (PDF).6. Is there a statistically significant difference in number of cigarettes smoked before and after a smoking cessation course? (Conduct a dependent t test.) The variables are labelled "Cigarettes Smoked pre" and "Cigarettes Smoked Post".7. Identify the independent and dependent variables.8. Write a null hypothesis.9. Write an alternative non-directional (2-tail) hypothesis.10. Interpret your results. Guidelines for interpreting dependentttests can be found inWhat to Include When Writing Up DependenttTest Results (PDF).In the following two screencasts, I complete problem #3 in Chapters 5 and 6 of your Munro text.Use the Health SPSS dataset to answer the following questions. Assume a .05 level of significance: 1. Is there a statistically significant difference in weight between men and women? (Conduct an independent t test.) 2. Identify the independent and dependent variables. 3. Write a null hypothesis. 4. Write an alternative non-directional (2-tail) hypothesis. 5. Interpret your results. Guidelines for interpreting independent t tests can be found in What to Include When Writing Up Independent t Test Results (PDF). 6. Is there a statistically significant difference in number of cigarettes smoked before and after a smoking cessation course? (Conduct a dependent t test.) The variables are labelled "Cigarettes Smoked pre" and "Cigarettes Smoked Post". 7. Identify the independent and dependent variables. 8. Write a null hypothesis. 9. Write an alternative non-directional (2-tail) hypothesis. 10. Interpret your results. Guidelines for interpreting dependent t tests can be found in What to Include When Writing Up Dependent t Test Results (PDF). In the following two screencasts, I complete problem #3 in Chapters 5 and 6 of your Munro text.   Microsoft Word - Dependent t results.docx What to include when writing up Dependent t-test results    1. Remind the reader of the type of test you used and the comparison  that was made. The IV and DV do not need to be specifically  identified as such, but those variables need to be included.       Example:     “An dependent t-test was used to compare differences in self- reported pain scores before and after medication was  administered.”    2. Report significance (or non-significance) between the conditions.       Example:     “There was a significant difference in the self reported pain scores  before medication was administered (M = 52.88, SD = 22.42)  compared with after the medication was administered (M = 41.12,  SD = 9.99) conditions; t(20) = 2.68, p = .014.” The difference was  significant at the p 	Greater than 	Greater than or equal to =	Equal to |x|	The absolute value of x p(A)	Probability of event A happening (marginal probability) (Continued)  ( Table 2-1 )   MATHEMATICAL SYMBOLS IN STATISTICS (Continued)	 Symbol	Meaning Mathematical Functions p(A|B)	Probability of event A happening if B happens (conditional probability) p(AB)	Probability of both event A and event B happening (intersection of A and B) p(AB)	Probability of event A happening or event B happening (union of A and B) Statistical Symbols a	Alpha: the significance level set for the study p	The p-value of the computed statistic H0	The null hypothesis HA	The alternative hypothesis  error	Type I error in hypothesis testing  error	Type II error in hypothesis testing N	Population size n	Sample size f	Frequency pi, p95	Percentile rank at the ith percentile, 95th percentile m	Mu, the population mean x	x-bar, the sample mean s2	Sigma squared, the population variance s	Sigma, the population standard deviation s2	Sample variance s	Sample standard deviation CI	Confidence interval df	Degrees of freedom 2	Chi-square r	Population correlation coefficient r	Sample correlation coefficient The researcher then attempts to answer the question by examining the data collected on the characteristics of interest in the study; in health research, this is usually about people or events. Sometimes researchers collect their own data, and sometimes they use data that may be avail- able from other studies or government sources. Once collected, the data must be organized, examined, and interpreted using well-defined procedures. Almost all quantitative studies involve data that are entered into a computer-based statisti- cal spreadsheet or database for subsequent data analysis. The logistics and time required to col- lect data, enter it into a statistical spreadsheet or database, and prepare it for data analysis are often greatly underestimated and poorly understood. Davidson (1996) recommends tak- ing control of the structure and flow of one’s data from the beginning. It is hoped that this will help eliminate faulty data leading to faulty conclusions. Appendix A contains an overview of how a good computer database can be cre- ated in SPSS (the process is similar for other programs such as SAS and STATA) and how one’s data can be initially cleaned so that it is suitable for analysis. What Is a Variable? In research, the specific characteristics or parameters of interest are commonly called variables. A variable is any characteristic that can and does assume different values for the dif- ferent people, objects, or events being studied. We measure the value of the variable for each participant in our study, and then record these measurements in a spreadsheet or database to form a data set. This set of observed mea- surements collected from study participants allows us to describe the variables. For exam- ple, demographic variables describe the basic characteristics of human populations or study samples such as age, gender, ethnicity, marital status, number of children, education level, employment status, and income. Each observa- tion within these variables is assigned a number using different rules: age is measured in years, gender is identified as male or female (can be coded “0” and “1”), income is measured in dol- lars earned per year, and so on. It is important to note that for a characteris- tic to be considered a variable, it is critical that everyone in a given sample does not have the same value for the characteristic. For example, gender, which can assume two values  (male  and female), is not a variable when studying a

Shakeel · Accepted Answer

Use the Health SPSS dataset to answer the following questions. Assume a .05 level of significance:
1. Is there a statistically significant difference in weight between men and women? (Conduct an independent t test.)
Answer
Yes, there is a statistically difference in average weight of men and women.
	Group Statistics
	
	GENDER
	N
	Mean
	Std. Deviation
	Std. Error Mean
	WEIGHT
	Male
	50
	171.1400
	25.10144
	3.54988
	
	Female
	50
	129.6800
	20.20653
	2.85763
	Independent Samples Test
	
	Levene's Test for Equality of Variances
	t-test for Equality of Means
	
	F
	Sig.
	t
	df
	Sig.

Use the Health SPSS dataset to answer the following questions. Assume a .05 level of significance: 1. Is there a statistically significant difference in weight between men and women? (Conduct an...

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