5-3 Assignment - Means, Test of Hypothesis

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You must use Excel to perform the regression analysis. Provide the answers under the space provided for each question. You must provide the Excel output for the question along with the answers. Round off the values on the output to three-five decimal places if appropriate.Examine the interaction effect between Bedrooms and Square Footage (SqFt) on house price in a multiple regression model. Include an interaction term for Bedroom × SqFt in the regression model alongside the individual terms for Bathrooms and SqFt. 12) Provide the Excel output below here: (2p)13) Analyze the p-value of the interaction term to determine if the interaction significantly contributes to predicting house price. (α = 0.05) (3p)14) Interpret the coefficient of the interaction term to understand how the effect of Bedroom on house price may change depending on the Square Footage of the home. (3p)15) Why does the main effect of Square Footage (SqFt) become insignificant when the interaction term (SqFt × Bedrooms)…

Assume there are 3,600 cases in the validation dataset, and 12% of these cases have a value of 1 for churn (the primary/positive event). Questions a) to c) are based on data for the 3,600 cases (see table below). Depth (% Contacted) Model Cumulative Gain Cumulative Lift Decision Tree 34.42 6.84 Logistic Regression Neural Network 20.19 4.01 34.62 6.88 Decision Tree Logistic Regression Neural Network 10 64.90 6.06 10 36.06 3.15 10 62.50 5.54 Decision Tree 15 73.96 1.82 Logistic Regression Neural Network 15 49.04 2.62 15 82.21 3.97 Decision Tree Logistic Regression 20 78.39 0.87 20 59.13 2.01 Neural Network 20 86.54 0.86 a) Which model has the highest Cumulative Lift at a depth of 20%? What is the lift? b) If the Cumulative Gain at a depth of 10% for the Decision Tree is converted to number of primary/positive event cases, what will be the number of cases? Show your calculation. c) If the Cumulative Gain at a depth of 15% for the Neural Network model is converted to number of…

3 Malthusian Model in the data This question relates to the following regression table taken from Ashref and Galor (2011). Standard errors are in parenthesis, and stars denote the level of statistical significance of the coefficients. Dependent variable is: Log years since Neolithic transition Log land productivity Log absolute latitude Mean distance to nearest coast or river Percentage of land within 100 km of coast or river Continent dummies Observations R² Log income per capita in: 1000 CE 1500 CE OLS (1) 0.159 (0.136) 0.041 (0.025) -0.041 (0.073) 0.215 (0.198) 0.124 (0.145) Yes 31 0.66 OLS (2) 0.073 (0.045) -0.021 (0.025) 0.060 (0.147) -0.111 (0.138) -0.150 (0.121) Yes 26 0.68 1 CE OLS (3) 0.109 (0.072) -0.001 (0.027) -0.175 (0.175) 0.043 (0.159) 0.042 (0.127) Yes 29 0.33 Log population density in: 1500 CE 1000 CE 1 CE OLS (4) 1.337** (0.594) -0.429 (1.237) 0.584*** (0.159) (0.110) 0.050 -2.140** (0.463) (0.801) 1.855** (0.820) OLS (5) Yes 31 0.88 0.832** (0.363) 0.364*** -0.237…

You must use Excel to perform the regression analysis. Provide the answers under the space provided for each question. You must provide the Excel output for the question along with the answers. Round off the values on the output to three-five decimal places if appropriate.Conduct a simple linear regression analysis for each independent variable associated with the external factors, using house price as the dependent variable. Make sure to use the indicator variables for the categorical data. (Significance level of α = 0.05). For each regression:22) Report the p-value of the independent variable and indicate whether it is a significant predictor of house price (based on the p-value being less than 0.05). (4p)23) Report the explained variability (R-squared value) for each variable, whether it is significant based on the p-value. (4p)24) Identify and list any variables that are not significant predictors of house price (i.e., those with p-values greater than 0.05). (2p)Conduct a multiple…

Newport, Inc. used Excel to run a least-squares regression analysis, which resulted in the following output: Regression Statistics Multiple R R Square Observations 0.7225 0.8500 30 Coefficients Standard Error T Stat P-Value 0.021 Intercept Production (X) 31,000 5.87 3,493 2.86 0.4640 14.30 0.000 a. What is Newport's total fixed cost? Total Fixed Cost b. What is Newport's variable cost per unit? (Round your intermediate calculations to 2 decimal places.) Variable Cost per unit c. What total cost would Newport predict for a month in which they sold 5,000 units? Total Costs d. What proportion of variation in Newport's cost is explained by variation in production? (Round your intermediate calculations to 2 decimal places.) Proportion of Variation

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SHOW YOUR WORK IN THE SPACES PROVIDED BELOW FOR FULL CREDIT. Properties of Normal Distribution: (3p) Write the word or phrase that best completes each statement or answers the question.  a. What is the total area under the normal curve?    b. The normal distribution is defined by two parameters. What are they?   c. What are the mean and standard deviation of the standard normal distribution?

Using the accompanying Home Market Value data, develop a multiple linear regression model for estimating the market value as a function of both the age and size of the house. State the model and explain R Click the icon to view the Home Market Value data. State the model for predicting MarketValue as a function of Age and Size, where Age is the age of the house, and Size is the size of the house in square feet. MarketValue = +Age+ (Size (Type integers or decimals rounded to three decimal places as needed.) The value of R2,, indicates that % of the variation in the (Type integers or decimals rounded to three decimal places as needed.) is explained by

In creating the T2 measure one mixes P* and T-bills to match the _____ of the market and in creating the M2 measure one mixes P* and T-bills to match the _____ of the market.    Group of answer choices   beta, alpha alpha, beta standard deviation, beta beta, standard deviation

Consider the following data for a dependent variable y and two independent variables, x1 and 12. 30 12 94 47 10 109 25 18 112 51 16 178 40 94 51 19 175 75 171 36 12 118 59 13 143 77 17 212 Round your all answers to two decimal places. Enter negative values as negative numbers, if necessary. a. Develop an estimated regression equation relating Y to ¤1. Predict y if æ1 = 35. b. Develop an estimated regression equation relating y to x2. ŷ = + Predict y if x2 = 25. ŷ = c. Develop an estimated regression equation relating y to ¤1 and 2. Predict y if x1 = 35 and x2 = 25. ŷ =

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What is answer for question b)

Least-Squares Regression Method; Scattergraph; Cost Behavior Professor John Morton has just been appointed chairperson of the Finance Department at Westland University. In reviewing the department’s cost records. Professor Morton has found the following total cost associated with Finance 101 over the last five terms: Professor Morton knows that there are some variable costs, such as amounts paid to graduate assistants, associated with the course. He would like to have the variable and fixed costs separated for planning purposes. Required: 1. Prepare a scattergraph plot. (Plot total cost on the vertical axis and number of sections offered on the horizontal axis.) 2. Using the least-squares regression method, estimate the variable cost per section and the total fixed cost per term for Finance 101. Express these estimates in the form Y = a + bX. 3. Assume that because of the small number of sections offered during the Winter Term this year. Professor Morton will have to offer eight…

Question 2 Listen Which linear regression equation matches the given data sets? Use your TI-84+ calculator to determine the equations. Hint: Click STAT and chose option 1: EDIT. Enter your data into L1 (explanatory variable) and L2 (response variable). Click STAT, choose CALC, and choose op- tion 8. Table A x y X Table B y 0 7.5 0 6.8 1 7.7 1 5 2 8 2 7.5 3 8 3 7 4 8.5 4 9 Table C Table D X y X y 0 11.2 0 15 1 7.7 1 77 2 8 NOT AN OPTION 3 4.5 3 6.5 4 4.6 4 3.2 ŷ=7.48+0.23x a. Table B ŷ-5.78+0.64x b. Table C y=10.48-1.64x c. Table A

In a simple linear regression based on 20 observations, it is found b1 = 3.05 and se(b1) = 1.30. Consider the hypothesis : H0 : β1 = 0 and HA : β1 ≠ 0 . Calculate the value of the test statistic.