Regression Analysis - 19751 Words

Confidence intervals and prediction intervals from simple linear regression The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. They have bivariate data detailing the stand s coffee sales (denoted by [pic], in dollars) and the maximum temperature (denoted by [pic], in degrees Fahrenheit) for each of [pic] randomly selected days during the past year. The least-squares regression equation computed from their data is [pic]. Tommorrow s forecast high is [pic] degrees Fahrenheit. The managers have used the regression equation to predict the stand s coffee sales for tomorrow. They now are interested in both a prediction interval for tomorrow s†¦show more content†¦The next term in the prediction interval formula is the standard error of the estimate, [pic]. It can be computed from the mean square error (MSE), which is given to be [pic]: [pic]. The last part of the prediction interval formula consists of the square root of the sum of [pic] and a fairly long expression. We do not need to compute the long expression, though, because we were given its value: [pic]. We have With this information, we can compute the [pic] prediction interval for the coffee sales given a maximum temperature of [pic] degrees Fahrenheit: [pic]. Upon simplification, this is the interval whose lower limit is approximately [pic] and whose upper limit is approximately [pic] 2. Because there s more precision involved in estimating the mean of a distribution than in predicting a particular observation from that distribution, we would expect the confidence interval to be narrower than the prediction interval. We can verify this by comparing the formulas for computing the intervals (shown near the top). As noted previously, the only difference between the prediction interval formula and the confidence interval formula is that the prediction interval formula has a [pic] in the sum underneath the square root, while the confidence interval formula does not. This makes the margin of error (the term following the [pic]) greater in the prediction interval formula than in the confidence interval formula, which means that theShow MoreRelatedApplication Of A Regression Analysis1241 Words   |  5 Pagesthe same explanatory variables appear in the log-log equations, which is in fact OLS is equivalent to seemingly unrelated regression, it is not possible to improv e the separate least-square estimation using a seemingly unrelated regression technique. Table 1 gives some details on the variables employed in the analysis. 4.1 Multicollinearity The purpose of regression analysis, we define first: N: Number of observation n: Number of independent variable y: Sample of ‘N’ observations on one dependentRead MoreRegression Analysis For A Dependence Method753 Words   |  4 PagesRegarding the testing of the hypotheses of this research, regression analysis or structural equation modelling techniques is best suited for a dependence method (Hair et al., 2014). We employed regression analysis to specify the extent to which the independent variables predicted the dependent variable. The analysis conducted in this study was therefore intended to test the hypotheses of the study. The regression output provided some measures which allow assessment of the hypotheses. Following fromRead MoreRegression Analysis1445 Words   |  6 Pages | LETTER OF TRANSMITTAL April 12, 2012 Dr. Abul Kalam Azad Associate Professor Department of Marketing University Of Dhaka Subject: Submission of a Report on regression analysis Dear Sir, Here is our term paper on regression analysis that you have assigned us to submit as a partial requirement for the course –â€Å"Business Statistics 1† Code no-212.While preparing this term paper; we have taken help from internet, books, class lectures and relevantRead MoreRegression Analysis for Demand Estimation1065 Words   |  5 PagesDemand Estimation by Regression Method – Some Statistical Concepts for application ( All the formulae marked in red for remembering. The rest is for your concept) In case of demand estimation working with data on sales and prices for a period of say 10 years may lead to the problem of identification. In such a case the different variables that may have changed over time other than price, may have an impact on demand more rather than price. In order to void this problem of identification whatRead MoreMlb Regression Analysis Data1212 Words   |  5 Pageseach of the variables specified in the model from the years 2003 to 2005. The question that I will be answering in my regression analysis is whether or not wins have an affect on attendance in Major League Baseball (MLB). I want to know whether or not wins and other variables associated with attendance have a positive impact on a team s record. The y variable in my analysis is going to be attendance for each baseball team. I collected the data for each team s average attendance for 2003-2005Read MoreRegression Analysis1447 Words   |  6 PagesREGRESSION ANALYSIS Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on price or vice-versa; will not be answeredRead MoreRegression And Correlation Analysis Paper Essay1128 Words   |  5 PagesStatistics Project PART C: Regression and Correlation Analysis A. Introduction and Summary Report: ALLSEASONS is a Chicago company that specializes in residential heating and cooling systems. Their call center has 100 employees who handle both inbound and outbound calls to schedule appointments for service technicians. Call center employees can schedule any type of appointment but they are assigned to one of three specialized teams, as noted below. During the first week of September the callRead MoreRegression Analysis1134 Words   |  5 Pagesflown on Northeast Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks: |Week |1 |2 |3 |4 |5 |6 | |Demand |17 |19 |15 |21 |20 |23 | Problem 7 [6] A careful analysis of the cost of operating an automobile was conducted by a firm. The following model was developed: Y = 4,000 + 0.20X where Y is the annual cost and X is the miles driven. a) If the car is driven 15,000 milesRead MoreRegression Analysis1301 Words   |  6 PagesIntroduction This presentation on Regression Analysis will relate to a simple regression model. Initially, the regression model and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain. Business Case In this instance, the restaurant chain s management wants to determine the best locations in which to expand their restaurant business. So far the mostRead MoreRegression analysis of oil price return3199 Words   |  13 Pagesï » ¿ Contents 1.0 Introduction and Motivation 2 2.0 Methodology 5 2.1. Descriptive Statistics 5 2.2 Matrix of pairwise correlation. 6 3.0 Model Specification 6 3.1 Linear Regression Model. 6 3.2 The Regression Specification Error Test 8 3.3 Non-linear models 9 3.4 Autocorrelation. 10 3.5 Heteroskedasticity Test 10 4.0 Hypothesis Testing 11 5.0 Binary (Dummy) Variables 11 6.0 Conclusion 13 Reference List 13 1.0 Introduction and Motivation Crude oil is one of the world’s most important natural