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- The accompanying dataset shows the monthly number of new car sales in the last three years. Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting sales Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting sales, where December is the reference month. Units=enter your response here+(enter your response here)•Year+(enter your response here)•Jan+(enter your response here)•Feb+(enter your response here)•Mar+(enter your response here)•Apr+(enter your response here)•May+(enter your response here)•Jun+(enter your response here)•Jul+(enter your response here)•Aug+(enter your response here)•Sep+(enter your response here)•Oct+(enter your response here)•Nov (Round to the nearest integer as needed.) Month Year Units Jan 1 39,813 Feb 1 40,078 Mar 1 47,439 Apr 1 47,298 May 1 49,212…can you please help with questions a-e i need helpIs It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
- Based on the Regression below, what is the relation between the GDP and gross fixed capital formation(GFCF), trade openness(trade openness), foreign direct investment (FDI). Explain by using A multiple linear regression model (Example y = b0 + b1x1 + b2x2 + …+ bkxk )We want to predict the probability of car accidents based on three risk factors: (i) average driving speed, (ii) weather, and (iii) user age. What is the most appropriate machine learning model for this case? Why? a. Linear regression b. Logistic regression c. K-means clusteringWhat does the correlation matrix for a multiple regression analysis contain?A. Multiple correlation coefficientsB. Simple correlation coefficientsC. Multiple coefficients of determinationD. Multiple standard errors of estimate
- To properly examine the effect of a categorical independent variable in a multiple linear regression model we use an interaction term. True O FalseSarah has some data and wants to run a linear regression model on it. Before she runs the test, she knows she needs to check to make sure all conditions are met. Based only on the plots below, what condition is not met? Data Scatterplot Normal Probability Plot Residual Scatterplot 25 100 Regression Standardoed Predcted Vale Observed Cum Prob Linearity O Normality Equal Variances O Independence O The plots do not show a problem with any of the listed conditions.Use the advertised prices for used cars of a particular model in the accompanying table to create a linear model for the relationship between a car's Age and its Price. Complete parts a through g. E Click the icon to view the data table. ...... e) You have a chance to buy one of two cars. They are about the same age and appear to be in equally good condition. Would you rather buy the one with a positive residual or the one with a negative residual? Explain. O A. The car with a positive residual is better because its actual price is above the predicted price for its age. Data table O B. The car with a positive residual is better because its actual price is below the predicted price for its age. OC. The car with a negative residual is better because its actual price is above the predicted price for its age. Age (yr) Price Advertised ($) OD. The car with a negative residual is better because its actual price is below the predicted price for its age. 1 17,619 14,999 16,018 13,988 15,009…
- Want to double check my answerWe have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)