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Select two variables from one data-set that are of interest to you. Explain briefly (two or three sentences) why you are interested in them. Then identify how each is constructed (its level of measurement, variable values etc.). Explain the strengths and limitations of such variable construction. (10 marks) 2. Produce a graph for each of the above two selected variables that can effectively show the variable’s distribution. For each of the two variables selected above, produce frequency tables and/or statistics that are appropriate for its level of measurement, and which best describe the variable’s distribution and central tendency. Make brief observations on each distribution based on the graphs and the statistics. (10 marks) 3. Select one variable on which you can test a hypothesis and construct a confidence interval (at the 95% level). Report what the results mean in plain English. (10 marks) 4. Select two categorical variables and construct a table showing the relationship of these variables to each other. Remember that you may need to combine some categories if any cells have too few cases. Report what you observe by inspecting the different percentages and values (observed and expected) in the table. Then carry out an appropriate test of statistical significance on the relationship between the two variables. Do the statistics confirm the observations you made before? Explain your answer. (30 marks) 5. Select two interval variables and explain why it would be sensible or interesting to study their relationship. You may want to choose a different data-set as some data-sets have more interval variables than others. Before using SPSS, explain what kind of relationship you would expect them to have. Then produce a graph with SPSS that could show their relationship – does the graph confirm your expectation? Calculate an appropriate measure of the strength of the relationships between the two variables and interpret what the results (not just the coefficient but its p-value as well) mean. Finally, construct a regression equation describing the relationships among these variables. Remember to specify which one is the explanat

USE THE BCS DATA TO ANSWER THE QUESTIONS

1. Select two variables from one data-set that are of interest to you. Explain briefly (two or three sentences) why you are interested in them. Then identify how each is constructed (its level of measurement, variable values etc.). Explain the strengths and limitations of such variable construction. (10 marks)

2. Produce a graph for each of the above two selected variables that can effectively show the variable’s distribution. For each of the two variables selected above, produce frequency tables and/or statistics that are appropriate for its level of measurement, and which best describe the variable’s distribution and central tendency. Make brief observations on each distribution based on the graphs and the statistics. (10 marks)

3. Select one variable on which you can test a hypothesis and construct a confidence interval (at the 95% level). Report what the results mean in plain English. (10 marks)

4. Select two categorical variables and construct a table showing the relationship of these variables to each other. Remember that you may need to combine some categories if any cells have too few cases. Report what you observe by inspecting the different percentages and values (observed and expected) in the table. Then carry out an appropriate test of statistical significance on the relationship between the two variables. Do the statistics confirm the observations you made before? Explain your answer. (30 marks)

5. Select two interval variables and explain why it would be sensible or interesting to study their relationship. You may want to choose a different data-set as some data-sets have more interval variables than others. Before using SPSS, explain what kind of relationship you would expect them to have. Then produce a graph with SPSS that could show their relationship – does the graph confirm your expectation? Calculate an appropriate measure of the strength of the relationships between the two variables and interpret what the results (not just the coefficient but its p-value as well) mean. Finally, construct a regression equation describing the relationships among these variables. Remember to specify which one is the explanatory variable and which is the response variable, and explain why. After using SPSS to generate the results, explain what they mean in plain English. (40 marks)

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