Suppose there are 2 quantitative free variables and 1 variable non free category. Non-free variables have 2 categories, namely 1 for the success category and 1 for the fail category. The method used to create models that describe relationships between variables is a binary logistic regression model. Perform parameter recovery for the model. Explain the stage
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- 1. An analyst ran a regression with four predictor variables. Variable description Variable Name Salary in R1000.00 Years at company Age in years Education in years SALARY YEARS AGE EDYEARS He suspects that AGE can be dropped from the model and he decided to employ forward stepwise regression. Show all the steps he has to do to get to a fitted response regression without age. 2. BIC, Bayesian information criteria or SBC, Schwarz' Bayesian Criteria, are the same. Give the aquations for AIC and BIC and explain the difference in these two equations in terms of the terms in the equations as well as the consequences. 3. Give a short description of measuring the actual predictive capabilities of the selected regression. model.Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shellsWhat is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?
- As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…(2)What would the consequence be for a regression model if theerrors were not homoscedastic?Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer
- Water is being poured into a large, cone-shaped cistern. The volume of water, measured in cm³, is reported at different time intervals, measured in seconds. A regression analysis was completed and is displayed in the computer output. Regression Analysis: cuberoot (Volume) versus Time Predictor Coef SE Coef Constant -0.006 0.00017 -35.294 0.000 Time 0.640 0.000018 35512.6 0.000 s=0.030 R-Sq=1.000 R-sq (adj)=1.000 What is the equation of the least-squares regression line? Volume = 0.640 - 0.006(Time) Volume = 0.640 - 0.006(Time) Volume = -0.006 + 0.640(Time) Volume = - 0.006 + 0.640(Time?)What is difference between regression model, and estimated regression equation?Describe the important characteristics of the variance of a conditional distribution of an error term in a linear regression. What are the implicationsfor OLS estimation?
- Explain what is meant by an error term. What assumptions do we makeabout an error term when estimating an ordinary least squares regression?The following data relate the sales figures of restaurant, to the number of customers registered that week: Week Customers Sales (SR) First 16 330 Second 12 270 Third 18 380 Fourth 14 300 a) Perform a linear regression that relates bar sales to guests (not to time). b) If the forecast is for 20 guests next week, what are the sales expected to be?Analysis of Variance Source DF SS MS Regression 1 02364 13 14 Residual Error Total 11.3240 What is the value of SSR (Sums of Squares for Regression)?