MATH533: Applied Managerial Statistics PROJECT PART C: Regression and Correlation Analysis Using MINITAB perform the regression and correlation analysis for the data on SALES (Y) and CALLS (X)‚ by answering the following questions: 1. Generate a scatterplot for SALES vs. CALLS‚ including the graph of the "best fit" line. Interpret. After interpreting the scatter plot‚ it is evident that the slope of the ‘best fit’ line is positive‚ which indicates that sales amount varies directly
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CHAPTER 13 CORRELATION AND REGRESSION ANALYSIS OUTLINE 4.1 Definition of Correlation Analysis 4.2 Scatter Diagram and Types of Relationships 4.3 Correlation Coefficient 4.4 Interpretation of Correlation Coefficient 4.5 Definition of Regression Analysis 4.6 Dependent and Independent Variables 4.7 Simple Linear Regression: Least Squares Method 4.8 Using the simple Linear Regression equation 4.9 Cautionary Notes and Limitations OBJECTIVES By the end
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1 CORRELATION & REGRESSION 1.0 Introduction Correlation and regression are concerned with measuring the linear relationship between two variables. 1.1 Scattergram It is not a graph at all‚ it looks at first glance like a series of dots placed haphazardly on a sheet of graph paper. The purpose of scattergram is to illustrate diagrammatically any relationship between two variables. (a) If the variables are related‚ what kind of relationship it is‚ linear or nonlinear
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http://www.mathsisfun.com/data/standard-normal-distribution-table.html (Z table) http://www.sjsu.edu/faculty/gerstman/StatPrimer/t-table.pdf (t table) Critical Values (Z) Level of Significance 1% 2% 4% 5% 10% Two Tailed ±2.56 ±2.32 ±2.05 ±1.96 ±1.64 Right tailed +2.32 +2.05 +1.75 +1.64 +1.28 Left tailed -2.32 -2.05 -1.75 -1.64 -1.28 Q1) A cinema hall has cold drinks fountain supplying Orange and Ditzy Colas. When the machine is turned on‚ it fills a 550ml cup with 500ml of the
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Project Part C Summary Report Management at the A. J. Davis company wishes the business to retain a competitive edge in the marketplace; ergo‚ they had statistical data collected regarding a selection of their customers who carry credit with the corporation. Based upon studies conducted on this data‚ it can be stated with a high degree of confidence that relationships do exist between certain discreet portions of the information gathered. Household Size vs. Credit Balances Predictably‚ the
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Project 1: Linear Correlation and Regression Analysis Gross Revenue and TV advertising: Pfizer Inc‚ along with other pharmaceutical companies‚ has begun investing more promotion dollars into television advertising. Data collected over a two year period‚ shows the amount of money Pfizer spent on television advertising and the revenue generated‚ all on a monthly bases. |Month |TV advertising |Gross Revenue | |1 |17 |4.1 | |2
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MATH 231: Basic Statistics Homework #5 – Correlation and Regression: 1). Bi-lo Appliance Super-Store has outlets in several large metropolitan areas in New England. The general sales manager aired a commercial for a digital camera on selected local TV stations prior ro a sale starting on Saturday and ending on Sunday. She obtained the information for Saturday-Sunday digital camera sales at the various outlets and paired it with the number of times the advertisement was shown on local TV stations
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PROJECT C MATH 533 INSTRUCTOR Prof AMIR SADRAIN 1. Generate a scatterplot for CREDIT BALANCE vs SIZE Regression Analysis: Credit Balance ($) versus Size 2. Determine the equation of the "best fit" line‚ which describes the relationship between CREDIT BALANCE and SIZE. There is a slight positive relationship between credit balance and size The regression equation is Credit Balance ($) = 2591 + 403 Size 3. Determine the
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Introduction to Linear Regression and Correlation Analysis Goals After this‚ you should be able to: • • • • • Calculate and interpret the simple correlation between two variables Determine whether the correlation is significant Calculate and interpret the simple linear regression equation for a set of data Understand the assumptions behind regression analysis Determine whether a regression model is significant Goals (continued) After this‚ you should be able to: • Calculate and
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Correlation and Regression Assignment Problem 1. a. Explain which variable you chose as the explanatory variable and discuss why. * The explanatory variable is the height. This is because I am assuming that as height increases‚ the weight will increase as well. So the weight is the dependent variable b. Produce a scatter plot and insert the result here. * Scatter plot c. Find the equation of the regression line‚ Write it in the form of y=a+bx‚ where a is the y-intercept
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