Regression analysis | Mathematics homework help - Vintage-paper.com

Question 1 Assignment: Data needed for questions 3-7 at bottom of page: In a regression analysis with multiple dependent variables, multicollinearity can be caused by: A strong nonlinear relationship between the dependent variable and one or more independent variables. A strong heteroskedastic relationship between the dependent variable and one or more independent variable. A strong linear relationship between two or more independent variables. None of the above. Question 2 Market researcher Ally Nathan is studying the relationships among price, type (classical or steel string), and consumer demand for acoustic guitars. She wants to find the relationship between demand and price, controlling for type.To determine this relationship, she should Run a simple regression of the dependent variable demand on the independent variable price and observe the coefficient on price. Run a simple regression of the dependent variable demand on the independent variable type and observe the coefficient on type. Run a multiple regression of the dependent variable demand on the independent variables price and type and observe the coefficient on type. Run a multiple regression of the dependent variable demand on the independent variables price and type and observe the coefficient on price. Question 3 The regression analysis relates US annual energy consumption in trillions of BTUs to the independent variable “US Gross Domestic Product (GDP) in trillions of dollars”.The coefficient on the independent variable tells us that: For every additional dollar of GDP, average energy consumption increased by 3,786 trillion BTUs. For every additional trillion dollars of GDP, average energy consumption increased 3,786 BTUs. For every additional trillion BTUs of energy consumption, average GDP increased by $3,786 trillion. For every additional trillion dollars of GDP, average energy consumption increased by 3,786 trillion BTUs. Question 4 The regression analysis relates US annual energy consumption in trillions of BTUs to the independent variable “US Gross Domestic Product (GDP) in trillions of dollars”.Which of the following statements is true? The y-intercept of the regression line is 62,695 trillion BTUs. The x-intercept of the regression line is $62,695 trillion. In the event that a thermonuclear war completely halts all economic activity and the US GDP drops to zero, energy consumption will sink to 62,695 trillion BTUs. None of the above. Question 5 The regression analysis relates US annual energy consumption in trillions of BTUs to the independent variable “US Gross Domestic Product (GDP) in trillions of dollars”. In a given war, if GDP is $7.4 trillion, expected energy consumption is: Around 91,501 trillion BTUs Around 90,711 trillion BTUs Around 28,016 trillion BTUs Around 467,729 trillion BTUs. Question 6 The regression analysis relates US annual energy consumption in trillions of BTUs to the independent variable “US Gross Domestic Product (GDP) in trillions of dollars”.How much of the variation in energy consumption can be explained by variation in the gross domestic product? About 99.99% About 97% About 94% Almost none of the variation in energy consumption can be explained by variation in GDP. Question 7 The data table at the bottom of the page tabulates a pizza paror’s advertising expenditures and sales for 8 consecutive quarters. The marketing manager wants to know how much of an impact current advertising will have on sales two quarters from now.While running a regression with the dependent variable “sales” and the independent variable “advertising lagged by two quarters”, how many data points can she use, given the available data? 6 7 8 9 For questions 3 through 6:Year GDP (in $trillions) Car Gas Mileage (in mpg) Energy Consumption ( in trillions of BTU) 1980 2.796 16 78,435 1981 3.131 16.5 76,569 1982 3.259 16.9 73,441 1983 3.535 17.1 73,317 1984 3.933 17.4 76,972 1985 4.213 17.5 76,705 1986 4.453 17.4 76,974 1987 4.743 18 79,481 1988 5.108 18.8 82,994 1989 5.489 19 84,926 1990 5.803 20.2 84,567 1991 5.986 21.1 84,640 1992 6.319 21 86,051 1993 6.642 20.5 87,780 1994 7.054 20.7 89,571 1995 7.401 21.1 91,501 1996 7.813 21.2 94,521 1997 8.318 21.5 94,969 1998 8.782 21.6 95,338 1999 9.274 21.4 96,968 US Energy Consumption (in trillion BTUs) vs. Gross Domestic Product ($trillions) Regression Statistics Multiple R 0.9709 R2 0.9426 Adjusted R2 0.9394 F test results Standard Error 1,889 F value Signif. F Observations 20 295.51 0.0000 Coefficients Std Error t Stat P-value Intercept 62,695 1,325 47.31 0.0000 GDP ($trillions) 3,786 220 17.19 0.0000 For Question

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