# Database

1) use the RRSP Database. Suppose a researcher wants to determine if the average of the total annual RRSP contribution is not equal to 2,389,147 ($*1,000). Use the province of New Brunswick in the RRSP Database as a sample, and perform a hypothesis test at a 1% level of significance. Assume that annual RRSP contribution is normally distributed in the population.

Data from RRSP Database (Region = "New Brunswick")

2) using the appropriate technique selected from this chapter and the RRSP Database, test to determine

a. if Alberta taxpayers contribute more towards their RRSP than Ontario taxpayers.

b. if Alberta's RRSP contribution population variance is greater than Ontario's.

Let α = 0.05 and assume the unknown population variances to be unequal.

a) Hypothesis Test:

b) Test of variances:

3) Do various financial indicators differ significantly according to type of company? Use a one-way ANOVA and the Financial Database to answer this question. Let Type of Company be the independent variable with seven levels (as listed in Analyzing the Databases in Chapter 1). Compute three one-way ANOVAs, one for each of the following dependent variables: Average Yield, Dividend per Share, and Average P/E Ratio. On each ANOVA, if there is a significant overall difference between Type of Industry, compute multiple comparisons to determine which pairs of types of industries, if any, are significantly different.

a) Average Yield vs. Type of Industry

4) Develop a regression model from the RRSP Database to predict the RRSP annual contribution by the Age of the Contributor for the province of Quebec. Discuss the model and its strength on the basis of statistics presented in this chapter. Now develop another regression model for the province of Ontario. Discuss the model and its strengths. Compare the two models. Does it make sense that age is a predictor of contribution? Why or why not?

a) Quebec

5) Develop a regression model using the Financial Database. Use Total Revenues, Average Yield, Dividend Growth, and Dividend per Share to predict the Average P/E Ratio for a company. How strong is the model? Which variables seem to be the best predictors?

7. use the Energy Resource Database to forecast year 36 of North American hydro energy production by using simple exponential smoothing. Let α = 0.20 and α = 0.80. Compare the forecast with the actual figure. Which of the two models produces the forecast with the least error? Repeat the same test using the European data set. Is there any difference between North America and Europe in energy production through Hydro?

8) In the RRSP Database, is the RRSP average annual contribution independent of province? The data given below are for the provinces from the Atlantic region of Canada. Use a chi-square test of independence to answer the question, α = 0.05.

Data from RRSP Database (Region = "New Brunswick")

2) using the appropriate technique selected from this chapter and the RRSP Database, test to determine

a. if Alberta taxpayers contribute more towards their RRSP than Ontario taxpayers.

b. if Alberta's RRSP contribution population variance is greater than Ontario's.

Let α = 0.05 and assume the unknown population variances to be unequal.

a) Hypothesis Test:

b) Test of variances:

3) Do various financial indicators differ significantly according to type of company? Use a one-way ANOVA and the Financial Database to answer this question. Let Type of Company be the independent variable with seven levels (as listed in Analyzing the Databases in Chapter 1). Compute three one-way ANOVAs, one for each of the following dependent variables: Average Yield, Dividend per Share, and Average P/E Ratio. On each ANOVA, if there is a significant overall difference between Type of Industry, compute multiple comparisons to determine which pairs of types of industries, if any, are significantly different.

a) Average Yield vs. Type of Industry

4) Develop a regression model from the RRSP Database to predict the RRSP annual contribution by the Age of the Contributor for the province of Quebec. Discuss the model and its strength on the basis of statistics presented in this chapter. Now develop another regression model for the province of Ontario. Discuss the model and its strengths. Compare the two models. Does it make sense that age is a predictor of contribution? Why or why not?

a) Quebec

5) Develop a regression model using the Financial Database. Use Total Revenues, Average Yield, Dividend Growth, and Dividend per Share to predict the Average P/E Ratio for a company. How strong is the model? Which variables seem to be the best predictors?

7. use the Energy Resource Database to forecast year 36 of North American hydro energy production by using simple exponential smoothing. Let α = 0.20 and α = 0.80. Compare the forecast with the actual figure. Which of the two models produces the forecast with the least error? Repeat the same test using the European data set. Is there any difference between North America and Europe in energy production through Hydro?

8) In the RRSP Database, is the RRSP average annual contribution independent of province? The data given below are for the provinces from the Atlantic region of Canada. Use a chi-square test of independence to answer the question, α = 0.05.

You'll get a 77.7KB .XLSX file.