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…

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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…

What is the F- test statistic and p-value

The production department is proposing the purchase of an automatic insertion machine. It has identified three machines and has asked the accountant to analyze them to determine which one has the best average rate of return. Machine A $43,529.50 $80,697.00 Machine B Machine C $64,675.95 310,925.00 268,990.00 431,173.00 Estimated average income Average investment Oa. Machine B Ob. Machine A Oc. Machine C Od. Machines B and C have the same preferred average rate of return.

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

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?

GIVEN THE FOLLOWING DATA, COMPUTE FOR THE FOLLOWING: 1. STRAIGHT LINE METHOD 2. ARITHMETIC GEOMETRIC CURVE 3. STATISTICAL PARABOLIC CURVE WRITE A RECOMMENDATION REGARDING THE RESULTS AND WHICH OF THE NETHOD IS BEST FIT FOR THE DATA. Nate: answer on a separate document. Use excel in compute. 2. Supposed this is Yc (straightline) 450,000 370,000 750,000 1,100,000 1,500,000 1,000,000 1,700,000 2,000,000 1,900,000 2,300,000 Yi + 1 (Geometric) YEAR SALES 415,000 1 356,000 703,556 1,023,400 1,308,905 900,573 1,504,789 1,705,932 1,895,890 2,094,256 450,000 370,000 750,000 1,100,000 1,500,000 1,000,000 1,700,000 2,000,000 1,900,000 2,300,000 2011 2012 2013 3 2014 4. 2015 2016 6. 2017 2018 8 2019 2020 10

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 =

The following data show the results of random sample of 10 batches of one pattern of product: Sample Batch Size X Support Costs Y 1 10  10$ 2 12 15 3 15 20 4 20 22 5 10 15 6 25 25 7 20 30 8 12 10 9 15 20 10 30 30 Calculate: Using regression analysis (Y=a + bx ) and predict support cost for a batch size X = $10 then what is Mixed Cost ?

What is answer for question b)

What is the p-value and do we reject or do we not reject?