If sales is the variable you are trying to explain and you have 2 independent variables of color and price. The color coefficient is 5, and the price coefficient is 20. You have an intercept coefficient of 500 and an r squared value of .2500. Using this multiple regression analysis, predict the amount of sales with a color rank of 5 and a price of 20 dollars.
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In multiple regression analysis you can have 2 or more independent variables.
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If sales is the variable you are trying to explain and you have 3 independent variables of video marketing, radio marketing, and price. The video marketing coefficient is 100, the radio marketing coefficient is 20, and the price coefficient is 20. You have an intercept coefficient of 500 and an r squared value of .6500. Using this multiple regression analysis, predict the amount of sales with 500 dollars being spent on video marketing, 500 dollars being spent on radio marketing, and the price is 100 dollars.
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If sales is the variable you are trying to explain and you have 2 independent variables of color and price. The color coefficient is 50, and the price coefficient is 20. You have an intercept coefficient of 5000 and an r squared value of .2500. Using this multiple regression analysis, predict the amount of sales with a color rank of 5 and a price of 200 dollars.
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The general purpose of multiple regression is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable.
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If the sum of squares regression is 100 and the sum of squares total is 500. Given that the sum of squares residual (or error) is 400 what is the r squared value?
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The r squared never decreases when a new variable is added to the regression equation.
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You are trying to interpret a t test for your multiple regression analysis. You had 10 total observations with 3 independent variables. The regression on the independent variables returned coefficients and t stats as follows ? Price coefficient: 10 and t stat is 7. Location coefficient: 2 and t stat is 3. Advertising coefficient is 100 and t stat is 1.0. Which of the following statements is an accurate interpretation of the situation when using an alpha of .10?
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You are trying to interpret a t test for your multiple regression analysis. You had 25 total observations with 2 independent variables. The regression on the independent variables returned coefficients and t stats as follows ? Price coefficient: 100 and t stat is 1.75. Location coefficient: 2 and t stat is 3.5. Which of the following statements is an accurate interpretation of the situation when using an alpha of .05?
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In multiple regression analysis, a dummy variable is one that takes the values 0 or 1 to indicate the absence or presence of some categorical effect. If the coefficient on a dummy variable is 15 that means that if the variable is present, we should expect our depedent variable to increase by 15.
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Compare two models. Model #1 has an r squared of .55 and an adjusted r squared of .50. Model #2 has an r squared of .52 and an adjusted r squared of .51. Given this information, what model should you use?
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Compare two models. Model A has an r squared of .85 and an adjusted r squared of .60. Model B has an r squared of .76 and an adjusted r squared of .63. Given this information, what model should you use?
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Compare 2 models. Model Q has an r squared of .90 and an adjusted r squared of .80. Model Z has an r squared of .94 and an adjusted r squared of .82. Given this information, what model should you choose?
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The independent variable is the variable you wish to explain.
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You are a realtor with a small business. You use a simple 2 variable linear regression analysis for quick first glance house price estimates using square footage of the house. You are asked by a customer what the price for his house would be given that his house has 1,500 square feet. The number you are trying to explain is the potential price of the house. You look at your numbers from his neighborhood and run the regression analysis. Your program returns the following data: R square: .5808, Square footage (X) Coefficient: 95.0, Observations: 20, Degrees of Freedom: 19, Intercept t stat: 1.87, Multiple R: .7723, Intercept Coefficient: 120,200, Square footage (X) Standard Error: .0288. What is the best first glance prediction you can give the customer about the value of his house given your 2 variable linear regression analysis?
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The r2 (r squared) value is equal to the sum of squares regression divided by ______________.
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You are doing regression analysis and your t stat for your independent variable is negative and significant. Thus, you can conlude that you have a linear relationship between the independent and dependent variables.
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Graph one shows a high number of observations that are very spread out but generally trending from the top left of the graph to the bottom right. Graph two has very few observations but the few that exist are tightly packed around a trend line sloping from the top left to the bottom right. Which of the following statement(s) are true?
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Graph one shows a number of observations that are very spread out but generally trending from the bottom left of the graph to the top right. Graph two has a number of observations that would be tightly packed around a trend line sloping from the top left to the bottom right. Which of the following statement(s) are true?
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Graph one shows a number of observations that all are directly on the trend line that moves from the top left of the graph to the bottom right. Graph two has a number of observations that would be tightly packed around, but not directly on, a trend line that runs straight across the graph. Which of the following statement(s) are true?
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What is your r square value given the following information: Sum of Squares Residual (or Error): 1,800, Intercept Coefficient: 50.25, Sum of Squares Regression: 300, Independent Variable Coefficient: .05.
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You have 4 observed values of the dependent variable in your sample. The values are 3, 6, 9, and 12. Calculate the SST.
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Which of the following terms describes the overall long term tendency of a time series?
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Time series models should NOT be used to mechanically extrapolate trends into the future without considering personal judgments, business experiences, changing technologies, etc.
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Florence20230101 04:42:1920230101 04:42:19homework 34 questions multiple choice complete 6 hours homework today 0 true or false deciding what distribution income should be example normative... Scroll to top 