Regression Project Report

Target is one of the largest retailers in the United States. Target wants to be able to give guests better quality products for a cheaper price. They also want to be the one stop shop. Target relies on their team members to keep the guests happy so they always come back again and again. Target Corp. is the nation 's #2 discount chain (behindWal-Mart). The fashion-forward discounter operates about 1,765 Target and SuperTarget stores in 49 states, as well as an online business at Target.com. Target and its larger grocery-carrying incarnation, SuperTarget, have carved out a niche by

(1) A study of the number of cars sold looked at the number of cars sold at 500

4) Use exponential regression (or find a constant ratio) to determine an equation for the data.

The instrumentation that is to be used is called the Your First College Year (YFCY) survey. This instrument is geared toward freshman college students, and it is a survey that concentrates on student experiences while in college with different areas in their college life. The questionnaire also recaps items from the freshman survey so that any changes can be studied. This particular survey is known to be beneficial for retention studies. This instrument contains 41 questions, and the survey will be administered on the computer instead of using the paper form. The rating scales that are used consists of, but not limited to, questions with ‘yes/no’ answers, ratings such with choices from ‘strongly disagree’ to ‘strongly agree’, and questions that will have rating choices that range from ‘can’t rate/no experience’ to very satisfied’. It will

In this project, many different statistics were used to try and predict the winners of the NCAA March Madness tournament. To do this, statistics were tested from the previous year to see if they moderately correlated to winning games. When a stat is moderately correlated, that means it relates to winning. Using a scatter plot, a graph that gives a visual of whether or not a stat is correlated, correlation coefficients were found for each stat. The correlation coefficient is a decimal that shows if a set of numbers is moderately correlated. After finding stats that were the closest to being moderately correlated, a metric was put together that used the best stats to determine which teams will win games. The stat that was the closest to being moderately correlated was the turnover ratio of the teams. Another stat that was almost moderately correlated was RPI, or Rating Percentage Index. This stat uses a team’s win percentage, their opponents’ win percentage, and their opponents’ opponents’ win percentage to rank teams. Also, missed field goal percentage was a stat that was used.

29. A distribution center for a chain of electronics supply stores fills and ships orders to retail outlets. A random sample of orders is selected as they are received and the dollar amount of the order (in thousands of dollars) is recorded, and then the time (in hours) required to fill the order and have it ready for shipping is determined. A scatterplot showing the times as the response variable and the dollar amounts (in thousands of dollars) as the predictor shows a linear trend. The least squares regression line is determined to be: yˆ= 0.76 +1.8x. A plot of the residuals versus the dollar amounts showed no pattern, and the following values were reported: Correlation r +0.90; R 2 = 0.81; standard deviation of the residuals is 0.48. What percentage of the variation in the times required to prepare an order for shipping is accounted for by the fitted line?

Linear regression is an approach for modeling the relationship between a scalar dependent variable Y and explanatory variables (or independent variables) denoted X. Function $f(X,W)=Y$ (shown below) can be learned to predict future values.

A researcher found a significant relationship between a person's age, a, the number of hours a person works per week, b, and the number of accidents, y, the person has per year. The relationship can be represented by the multiple regression equation y = -3.2 + 0.012a + 0.23b. Predict the number of accidents per year (to the nearest whole number) for a person whose age is 42 and who works 46 hours per week.

You provided no discussion about TAXYPR, TGEG, and DMC in Table 4. How can you insert TGEG and DMC into the regression equation as explanatory variables?

3. Compare the financial results from Question 1 and 2 and highlight your observation regarding contribution. Based on your calculations, what is the relationship between volume and cost?

We collected fingerprints from a tile found at the crime scene. We found two fingerprints and compared them to the fingerprints in the suspect database. Both fingerprints had an arch ridge pattern. Using this information, we can immediately exclude the Maine South Redhawk, the New Trier Trevian, and the Waukegan Bulldog. None of these suspects had any fingers with arch ridge pattern. The Niles North Viking and the Glenbrook North Spartan each have a single finger with the arch ridge pattern. We found that neither print matched the prints found at the crime scene. In analyzing the Loyola Rambler’s fingerprints, we found two matches. The right index finger and the right middle finger matched the fingerprints found at the scene. Many minutiae matched. For example, the right middle finger had multiple bridge minutiae in unique places. These bridges were identified in similar places on one print found at the scene. The right index finger also had bifurcations in unique placements. These matched one of the prints found at the scene. Based on this analysis, the suspect who most likely committed the crime was the Loyola Rambler.

Using MINITAB perform the regression and correlation analysis for the data on SALES (Y) and CALLS (X), by answering the following questions:

So you first predict the independent variable, then look at the established relationships between that independent variable and the dependent ones to predict what the dependent variables will be. You then develop an equation that summarizes the effects of predictor variables.

Biostatistics is important to study as undergraduates delve deeper into their studies of Biology and learn how the study of life is integrated into more than just their college-level science courses. Looking into the use of statistics at a scientific level at this stage of our education is preparing determined and enthusiastic students for the world of medicine as one day we will have to read and analyze sets of data and more than likely give the statistics of our patients’ issues. Biostatistics is allowing students to explore the world of medicine using a different approach, mathematically and critically. The purpose of this experiment was to determine if a significant multiple regression exists between my 3 quantitative variables, Weight, Height and Age and to determine the best regression model to use when making predictions.

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