5-3 Assignment - Means, Test of Hypothesis

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.Questions for Real Estate Case Study-Model Building:As preliminary analysis the dataset includes information on 50 homes currently for sale, but some homes have unusually high prices, square footage, and lot sizes. To refine the dataset for analysis, apply the following exclusion criteria:- Exclude any home with a price greater than $1,000,000.- Exclude any home with square footage (SqFt) greater than 3000 ft².- Exclude any home with a lot size greater than 10,000 ft².After performing these exclusions, how many homes remain in the dataset?1) Show the observations that excluded (2p)2) How many categorical variables are present in the dataset? How will you incorporate these categorical variables into the regression…

More real estate Consider the Albuquerque home sales from Exercise 29 again. The regression analysis gives the model Price = 47.82 + 0.061 Size. a) Explain what the slope of the line says about housing prices and house size. b) What price would you predict for a 3000-square-foot house in this market? c) A real estate agent shows a potential buyer a 1200-square- foot home, saying that the asking price is $6000 less than what one would expect to pay for a house of this size. What is the asking price, and what is the $6000 called?

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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…

You are setting up the shown ONE-WAY data table to calculate the NPV of a hotel project at various rates of return (8.25% to 12.5%). What range would you highlight before selecting:   Data →  What-if Analysis  →  Data Table…           Question 51 options:   B15:C32   C14:C32   B14:B32   B14:C32

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

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 =

Consider the following set of dependent and independent variables. Complete parts a through c below. y 9 11 D 15 15 21 24 28 32 X1 2 6 6 8 6 11 15 20 X2 16 10 14 11 3 8 7 4 a. Using technology, construct a regression model using both independent variables. y= (15.3952) + (1.0092) x₁ + (-0.5869)x2 (Round to four decimal places as needed.) b. Test the significance of each independent variable using α = 0.05. Test the significance of x₁. Identify the null and alternative hypotheses. Ho B₁ = H₁ B₁ 0 0 (Type integers or decimals.) Calculate the appropriate test statistic. The test statistic is 4.20 (Round to two decimal places as needed.) Determine the appropriate critical value(s) for α = 0.05. The critical value(s) is (are) ☐ (Round to two decimal places as needed. Use a comma to separate answers as needed.)

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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…

Using the accompanying data of a TV viewing survey, conduct a hypothesis test to determine if married individuals watch less TV than single individuals. Assume that the population variances are unequal. Click the icon to view the TV viewing survey Is there sufficient evidence at the 0.05 level of significance that the married individuals watch less TV than single individuals? Determine the null hypothesis, H. and the alternative hypothesis. H, Let married people be population 1 and singles be population 2. H₂ 1-1 0 AP 0 (Type integers or decimals. Do not round.) Survey data Compute the test statistic. (Round to two decimal places as needed.) Marital Status Hrs of TV viewing per week Marital Status Hrs of TV viewing per week Married 6 Single 38 Married 17 Single 18 Mamed 13 Single 14 Mamed 21 Single 10 Married 11 Single 14 Married 14 Single 10 Married 15 Single 35 Married 7 Single 20 Married 18 Single Married 28 Single Single 13 Single 18 Single 10 Single 15 Single 14 - X

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.