Describe the relationship indicated by the correlation coefficient, r = 0.46. ○ Loosely clustered in a negative linear fashion ○ Loosely clustered in a positive linear fashion ○ No linear relationship ○ Tightly clustered in a negative linear fashion ○ Tightly clustered in a positive linear fashion

Determine the PRF.

Interpret the coefficients and calculate the price elasticity of soft drink demand.

The following data was collected to explore how a student's age and GPA affect the number of parking tickets they receive in a given year. The dependent variable is the number of parking tickets, the first independent variable (1) is the student's age, and the second independent variable (x2) is the student's GPA. Effects on Number of Parking Tickets Age GPA Number of Tickets 17 2 1 17 2 2 18 2 4 20 2 5 20 3 5 22 3 6 233 22 3 6 3 7 25 4 7 Copy Data Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant. Answer How to enter your answer (opens in new window) Selecting a checkbox will replace the entered answer…

Find the degrees of freedom in a regression model that has 10 observations and 7 independent variables

The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1) is the square footage, the second independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2750 5 14 296600 2696 5 11 294400 2523 4 7 281400 2057 4 7 240600 1797 4 5 208600 1767 4 5 196400 1684 4 4 171900 1554 3 4 162800 1521 3 3 144900 Copy Data Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.01 level of significance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers to three decimal places. Otherwise,…

The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1) is the square footage, the second independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house. Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2750 5 14 296600 2696 5 11 294400 2523 4 7 281400 2057 4 7 240600 1797 4 5 208600 1767 4 5 196400 1684 4 4 171900 1554 3 4 162800 1521 3 3 144900 Step 1 of 2: Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places. Copy Data

Past class data has shown that the regression line relating the final exam score and the midterm exam score for students who take statistics from the College of Information Technology and Engineering from Dr. Kalaw is: final exam = 50 + 0.5 × midterm   One interpretation of the slope is a. students only receive half as much credit (.5) for a correct answer on the final exam compared to a correct answer on the midterm exam. b. a student who scored 0 on the midterm would be predicted to score 50 on the final exam. c. a student who scored 10 points higher than another student on the midterm would be predicted to score 5 points higher than the other student on the final exam. d. a student who scored 0 on the final exam would be predicted to score 50 on the midterm exam.

Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed actress/actor ages in various years, find the best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years. Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? Best Actress 27 30 30 61 30 32 46 28 61 22 43 56 D Best Actor 42 39 38 45 51 49 59 51 38 57 45 34 Find the equation of the regression line. y = + (Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.) The best predicted age of the Best Actor winner given that the age of the Best Actress winner that year is 43 years is years old. (Round to the nearest whole number as needed.) Is the result within 5 years of the actual Best Actor winner, whose age was 45 years? the predicted age is the actual winner's age.

18 Given that the sum of the squared deviations of EDUC is  9025.6   The standard error of the slope coefficient is ________.   a 0.1524           b 0.1249           c 0.1024           d 0.0839