Suppose we run the following OLS regression JobStr = B1 + B2 M+ B3 Erp + B, Edu + u to explain an individual's job stress level by their gender (M for male dummy), work experience (Exp) and education (Edu), using a sample of 300 observations. Suppose that the F statistic for the joint significance of the model is 30 and the RSS of the fitted model is 250. What is the TSS of the model?
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- What is an experiment?he average height of a large group of children is 43 inches, and the SD is 1.2 inches. The average weight of these children is 40 pounds, and the SD is 2pounds. The correlation between the two variables is r = 0.65.A scatter diagram is drawn, with height on the horizontal axis and weight on the vertical axis. The scatter diagram is football-shaped. The regression line forpredicting weight based on height is drawn through the scatter.Q. Predict the weights and the typical size of the error for those predictions ineach of the following case: Suppose a child’s height is at the 29th percentile of all heights. Using regression, our best guess is that the child’s weight (measured in pounds) is at the___________________ percentile compared to all other children.D& T LTD marketing team needed more information about the effectiveness of their 3 main mode of advertising. To determine which type is the most effective, the manager collected one week’s data from 25 randomly selected stores. For each store, the following variables were recorded: Weekly gross sales Weekly expenditure on direct mailing (Direct) Weekly expenditure on newspaper advertising (Newspaper) Weekly expenditure on television commercials (Television) Following is the regression output based on the above-mentioned data. SUMMARY OUTPUT Regression Statistics Multiple R 0.442…
- A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower). Model 1: mpg vs engine displacement The regression equation is mpg_city=33.8 - 0.0622*displacement S = 3.10179 R-squared = 66.9% Model 2: mpg vs horsepower The regression equation is mpg_city=32.4 - 0.0579*horsepower S = 3.30296 R-squared = 52.9% The variable horsepower is better because it has a higher residual standard error (S=3.30296) and a lower R-square (52.9%). The displacement variable is better because it has a higher R-square (66.9%). (C) The variable horsepower is better because it has a higher residual standard error (S=3.30296).A study was made of movies in 2005. For each movie, the following were recorded: its budget (the amount it cost to make, in millions of dollars), its running time (from start to finish, in minutes), and the genre (drama, comedy, action, etc.). The scatterplot below shows the budget vs. running length for all movies. Two regression lines are shown on the scatterplot, for predicting budget from running length. The lower line is for movies of the "drama" genre, while the upper line is for movies of all other genres. Which of the statements below do you most strongly agree with? (a) The difference in budget between dramas and other movies of the same length remains constant as the movies get longer. (b) Dramas cost less to make than other movies of the same length, and that difference increases as the movies get longer. (c) Dramas cost more to make than other movies of the same length, and that difference increases as the movies get longer. (d) Dramas cost more to make than other movies of…A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?
- The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.Which of the given independent variables is/are significant? * avil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 1 1 78.0 15500 81.3 16000 82.4 2 3 79.0 80.0 85.0 16200 83.4 3 17500 87.9 89.9 89.1 84.1 89.0 89.2 4 18000 90.3 5 16,300 84.2 3 17000 87.0 4 17900 88.1 GPA and years of experience GPA, years of experience and civil service ratings intercept, GPA, years of experience and civil service ratings O years of experience and civil service ratingsResearcher asked waitresses to wear different colored T-shirts on different days for a six-week period and recorded the tips left by male customers. The results show that male customers gave significantly bigger tips to waitresses when there were wearing red. For this study, identify the independent and dependent variable (two answers)?The linear regression alanysis shows that the pressure, measured in millibars is the explanatory variable, and the wind speed, measured in miles per hour is the reponse variable. The scatter plot shown below represents wind speed contrasted with the pressure in a hurricane, each dot in the scatter plot represents a hurricane. This figure reveals that there is a negative linear association between the two variables with a sample correlation coefficient of r = -0.9498. y = -1.095x + 1152.3 is the regression model that is used to describe the relationship between the two variables, the slope is a = -1.095, the y-intercepts is b = 1152.3, and the coefficient of determination is ?2 = 0.9022. The slope reveals that as the atmospheric pressure increases by one milibar, the predicted speed of wind will decrease by -1.095. Analysing the coefficient of determination displayed in the figure below, ?2 = 0.9022 means that approximately 90% of the variability on wind speed is explained by the…
- Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.885 and the least squares regression line is y = 0.151x+ 13.311. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.5 17.1 17.3 17.0 17.5 17.4 17.2 17.3 17.4 17.4 17.3 27 25 26 25 28 26.75 26.25 26.75 26.75 27.25 27.25 a ... (a) Compute the coefficient of determination, R?. R =% (Round to one decimal place as needed.) !!The options for part b are: head circumference or heightWe expect a car’s highway gas mileage to be related to its city gas mileage (in mpg). Data for all 12091209 vehicles in the government’s 2016 Fuel Economy Guide give the regression line highway mpg=7.903+(0.993×city mpg)highway mpg=7.903+(0.993×city mpg) for predicting highway mileage from city mileage. (a) What is the slope of this line? (Enter your answer rounded to three decimal places.) What does the numerical value of the slope tell you? On average, highway mileage decreases by 0.9930.993 mpg for each additional mpg in city mileage. On average, highway mileage increases by 0.9930.993 mpg for each additional mpg in city mileage. For every 7.9037.903 mpg in city gas mileage, highway gas mileage increases about 0.9930.993 mpg. Highway gas mileage increases with city gas mileage by 7.9037.903 mpg for each additional mpg in city mileage. On average, highway mileage increases by 7.9037.903 mpg for each additional mpg in city mileage. (b) What is the intercept?…