Stock Beta. In June 2021 Yahoo Finance reported the beta value for Coca-Cola was .61 (Yahoo Finance website). Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of the S&P 500. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. The data show the monthly percentage returns for the S&P 500 and the Coca-Cola Company for July 2020 to June 2021. Answer the following questions. 1. Develop a scatter diagram with the S&P % Return as the independent variable. Label this graph as "Scatterplot Coke vs S&P" 2. What does the scatter diagram developed in part (1) indicate about the relationship between the returns of the S&P 500 and those of the Coca-Cola Company? State your answer here 3. Develop the least squares estimated regression equation. State your answer here..use actual names of the variables instead of saying YHAT and X. 4. Provide an interpretation for the slope of the estimated equation (i.e., the beta). State your answer here 5. Is your beta estimate close to .61? If not, why might your estimate be different? State your answer here

Use the Financial database from “Excel Databases.xls” on Blackboard.  Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company.  Use Excel to develop the multiple linear regression model.  Assume a 5% level of significance.   Which independent variable is the strongest predictor of the average P/E ratio of a company?     A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share   Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…

Find the regression​ equation, letting the first variable be the predictor​ (x) variable. Find the best predicted Nobel Laureate rate for a country that has 78.5Internet users per 100 people. How does it compare to the​ country's actual Nobel Laureate rate of 1.1 per 10 million​ people? Internet Users Per 100 Nobel Laureates80.3     5.679.6     24.277.3     8.644.6     0.183       6.138.3    0.190.1   25.189.1   7.680.1   8.983.3   12.753.2   1.976.6   12.756.8   3.379.5   1.591.5   11.493.7   25.465.1   3.157.8   1.967.9   1.794.3   31.785.3   31.387.7   1977.7   10.8

Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion).  Use the horizontal axis for the customer age to graph.  Find the linear regression line that models the data by each type of customer.  Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales.  What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y  / Change in x

Find the regression​ equation, letting the first variable be the predictor​ (x) variable. Find the best predicted Nobel Laureate rate for a country that has 79.4 Internet users per 100 people. How does it compare to the​ country's actual Nobel Laureate rate of 1.2 per 10 million​ people? Internet Users Per 100 Nobel Laureates 79.3     5.6 80.5     24.2 78.8     8.7 45.2     0.1 82.5     6.2 38        0.1 90.3     25.4 88.6     7.6 80.3     9.1 83.1     12.6 53.3     1.9 76.3     12.7 56.6     3.3 78.5     1.5 91.6     11.5 93.4     25.2 65.3     3.1 57.3     1.9 68.2     1.7 94.8     31.6 85.4     31.3 86.4     19.1 78.3     10.9   2) Check Image

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y (b) x = 649 feet (d) x = 724 feet 766 620 520 508 494 484 (a) x = 503 feet 51 46 44 43 39 38 (c) x = 318 feet Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. O B. O C. O D. 60- 60- 60 60- 0- 800 800 800 Height (feet 800 Height (feet) Height (Teet) Stories Stories:

A social scientist collects information about counties in California and finds that the correlation between average income of the county and a rating of healthcare quality in the county is 0.78. A scatterplot of the two variables is football shaped. A particular county has an average income that is 0.4 SDs above the average of all counties. Using regression, we would predict that its healthcare quality is _________ SDs above the average healthcare quality for all counties, and that it is therefore at the _________ percentile of healthcare quality among all counties. Choose the answer below to fill in the two blanks. Group of answer choices 0.31; 24th 0.78; 58th 0.78; 79th 0.31; 62nd      PreviousNext