A financial analyst is examining the relationship between stock prices and earnings per share. She chooses sixteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with X denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least- squares regression line to be ŷ=-0.231+0.045x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x Current stock price, y (in dollars) (in dollars) 58.47 2.17 30.64 1.44 41.03 1.45 41.26 1.05 26.04 0.80 42.80 1.84 18.10 0.77 21.73 0.62 29.68 0.74 32.97 1.64 37.74 1.56 57.98 2.70 38.05 1.16 49.40 1.59 15.49 0.59 51.67 2.65 Send data to calculator ✓ Send data to Excel Current stock price (in dollars) Based on the analyst's data and regression line, complete the following. 05 0 10 30 x x x ** 40 Earnings per share (in dollars) × × 50 60 (a) For these data, current stock prices that are less than the mean of the current stock prices tend to be paired with values for earnings per share that are (Choose one) the mean of the values for earnings per share. (b) According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of how many dollars in current stock price? ☐

For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…

Management at Jagoda Wholesalers, in Calgary, Canada, has used time-series regression based on point-of-sale data to forecast sales for the next 4 quarters. Sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective quarters. Seasonal indices for the four quarters have been found to be1.30, .90, .70, and 1.10, respectively.

A financial analyst is examining the relationship between stock prices and earnings per share. She chooses fifteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least- squares regression line to be y = -0.137 +0.043x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x (in dollars) Current stock price, y (in dollars) 59.10 2.35 30.89 1.34 40.95 1.76 29.50 1.02 48.19 1.63 37.29 1.68 21.65 0.63 15.95 0.63 1- 26.45 1.02 0.5 32.26 1.53 17.72 0.78 42.86 1.50 Earnings per share (in dollars) 36.45 1.17 57.56 2.84 40.32 1.06 Current stock price (in dollars)

a. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.

Name Her Age His Age Barack and Michelle Obama 45 47 George W. and Laura Bush 54 54 Bill and Hillary Clinton 45 46 Ronald and Nancy Reagan 55 64 Gerald and Betty Ford 59 69 Lyndon and Lady Bird Johnson 56 61 John and Jacqueline Kennedy 31 43 Dwight and Mamie Eisenhower 50 55 (c) Remove the outlier and compute the least-squares regression line for predicting the president's age from first lady's age. Round the slope and y -intercept values to at least four decimal places. Regression line equation: =y

You are studying how a penguin's bill length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. Given the information below, choose the formula for the least squares regression line. b₁ = 87.42 bo = 362.31 x = 43.92 y = 4202.0 O Bill Length = 87.42 Body mass + 362.31 O Bill Length = 87.42*4202.0 + 362.31 O 4202.0 = 362.31*43.92 +87.42 O Body mass = 87.42 * Bill Length + 362.31 O Body mass = 362.31 *Bill Length + 87.42 O Body mass = 362.31 43.92 + 87.42

Suppose John is a high school statistics teacher who believes that watching many hours of TV leads to lower test scores. Immediately after giving the most recent test, he surveyed each of the 24 students in his class and asked them how many hours of TV they watched that week. He then matched each student's test grade with his or her survey response. After compiling the data, he used hours of TV watched to predict each student's test score. He found the least-squares regression line to be ?̂ =−1.5?+85y^=−1.5x+85. He also calculated that the value of ?r, the correlation coefficient, was −0.61. Which of the choices identifies the correct value of the coefficient of determination, ?2R2, and gives a correct interpretation of its meaning?

A financial analyst is examining the relationship between stock prices and earnings per share. She chooses sixteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least-squares regression line to be y = -0.224+0.044x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x (in dollars) 30.13 42.76 58.46 37.50 57.30 27.60 30.17 41.82 50.09 38.18 22.41 33.44 39.66 14.13 17.85 53.57 Send data to calculator V Current stock price, y (in dollars) 1.41 1.37 2.25 1.54 2.64 0.90 0.79 1.04 1.56 1.15 0.65 1.72 1.80 0.51 0.71 2.74 Send data to Excel Current stock price (in dollars) Based on the analyst's data and regression line, complete the following. 2.5- 2+ 1.5. 0.5- 0…