The director of a local Tourism Authority would like to know whether a family’s annual expenditure on recreation (y), measured in $000s, is related to their annual income (x), also measured in $000s. In order to explore this potential relationship, the variables x and y were recorded for 10 randomly selected families that visited the area last year. The results were as follows: Week 1 2 3 4 5 6 7 8 9 10 x 41.2 50.1 52.0 62.0 44.5 37.7 73.5 37.5 56.7 65.2 y 2.4 2.7 2.8 8.0 3.1 2.1 12.1 2.0 3.9 8.9 The summary statistics for these data are: Sum of x data: 520.4 Sum of the squares of x data: 28431.42 Sum of y data: 48 Sum of the squares of y data: 343.74 Sum of the products of x and y data: 2858.63 Calculate the least squares line of y on x and draw the line on the scatter diagram.

Look the image and answer the following questions: a. What is the independent variable (IV)? b. How many levels do we have for the IV? C. What is the level of measurement for the IV? d. What is the DV (remember, the DV is the data)? e.What is the level of measurement of the DV? f. Why a Bar graph is the best graph to display this data? Explain g. Construct a Bar graph that incorporates all the information in this table. h. Looking at your graph, do you think that the relaxation training really worked. Explain your answer.

Given the weekly price and quantity (Qx and (Px) data for Andy’s ice cream over the past 12 weeks and the price of another ice-cream flavor (Po) as: Qx 84 82 85 83 82 84 87 81 82 79 82 78 Px 8.50 9.00 8.75 9.25 9.50 9.25 8.25 10.00 10.00 10.50 9.50 10.25 Po 5.25 6.00 6.00 6.50 6.25 6.25 5.25 7.00 7.25 7.25 6.75 7.25 a. Use excel regression to estimate the weekly demand for Andy’s ice-cream (attach the output of the regression estimate (Hint: Use regression in excel to find the estimated demand function: Enter the data in excel, click on Data Analysis, and use the regression command). b. Are the coefficients on the two prices statistically significantly different from zero at the 5% significance level? How do you know? c. What the R2? d. Explain what R2 means.

The following data represents the winning percentage (the number of wins out of 162 games in a season) as well as the teams Earned Run Average, or ERA.The ERA is a pitching statistic. The lower the ERA, the less runs an opponent will score per game. Smaller ERA's reflect (i) a good pitching staff and (ii) a good team defense. You are to investigate the relationship between a team's winning percentage - Y, and its Earned Run Average (ERA) - X. Winning Proportion - Y Earned Run Average (ERA) - X 0.623457 3.13 0.512346 3.97 0.635802 3.68 0.604938 3.92 0.518519 4.00 0.580247 4.12 0.413580 4.29 0.407407 4.62 0.462963 3.89 0.450617 5.20 0.487654 4.36 0.456790 4.91 0.574047 3.75 (b) Use R-Studio to find the least squares estimate of the linear model that expressed a teams winning percentage as a linear function of is ERA. Use four decimals in each of your answers.Y^i = ? + - Xi(e) A certain professional baseball team had an earned run average of 3.45 this past…