# Viewing papers 1-30 of 460 for correlation AND and AND regression

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#### Regression Analysis: Real Estatee Sales Essay…

… The negative slope of the linear regression line verifies this relationship between the two variables. However, the graph also demonstrates that this correlation is extremely weak. The coefficient of determination (R2), which measures the "fit" of the model, was calculated at 0.12043. This means that the regression line explains only about 12% of the variation in the model.

As predicted in the hypothesis, the sales price of houses in this Real Estate listing generally decreased as the distance from the city increased. However, according to the R2 value, only 12% of the data set applies to the hypothesis that sales prices will decrease as the distance from the city increases. One prime example of this weak correlation lies in the large variability of sales prices…. [read more]

#### Regression Analysis Is the Technique Term Paper…

… Regression analysis is the technique used to express the linear relationship between two variables. With this technique we can estimate the value of the dependent variable (Y) based on a selected value of the independent variable (X). This kind of analysis is mostly used to determine if sales numbers of a product a are related to the number of TV advertisements for that particular product during a specific timeframe. The mathematical equation for the line used to estimate the value of variable Y on the basis of variable X is also known as the regression equation (general form of linear regression equation: Y1 = a + bX) (Lind, Marchal, & Wathen, 2005).

Y1

= read Y prime, is the predicted value of Y for a…. [read more]

#### Regression Analysis the Methodology for Analyzing Data Research Paper…

… Regression Analysis

The methodology for analyzing data is just as important as having a strong foundation for collecting. Within qualitative and quantitative research applications, the use of statistics is a common practice. One of the most used statistical analysis techniques is what is known as regression analysis. This current report explores the processes within regression analysis in what environments the methodology is appropriate for use in to analyze data.

Regression analysis uses statistical formulas in order to show a correlation between particular variables. Sykes (2012) posits that it is "a statistical tool for the investigation of relationship between variables." It is essentially a way to quantify the relationship between the variables and manipulate environmental changes to forecast what impact they would have on such variables…. [read more]

#### Regression, Correlation: Effect of IQ on GPA Essay…

… In this case, for instance, a child's working memory was one of the subscales used to measure their IQ. It is possible that a child would intentionally choose to forget some aspects, such as those taught in a lesson where they were either punished or humiliated in class, and only remember those they consider worth remembering. If their working memory index is based on this element, it is likely that the IQ results obtained will be inaccurate.

Reliability, on the other hand, requires the employment of standardized data collection and sampling procedures as a way of ensuring that consistency is maintained (Walker, 2011). It is possible that errors were made in reporting or transfer of data from the computerized database into the reportable forms. Moreover,…. [read more]

#### Statistical Analysis With Regression Term Paper…

… Regression Analysis of Auto Sales

Statistical Analysis of Auto Sales

Eleven attributes all pertaining to the sales of automobiles sold from the first quarter of 1980 to the fourth quarter of 2004 form the data set and the basis of this analysis and the creation of a series of multiple regression models. The objective of the creation of a multiple regression equation is to predict sales of automobiles. Stepwise multiple regression was used, yielding the following models as shown in Table 1. Using stepwise regression the variable SPSS Version 13 for Windows' stepwise regression technique yielded Personal Income (pi) as the independent variable that most influenced sales of new autos (unitsales) and as a result the first model created includes only this variable. Finance Rate…. [read more]

#### Multiple Regression it Is Hypothesized Assessment…

… According to the B. weights, the regression equation is as follows:

Predicted Perceived Stress = -.24 Self-Efficacy - .34 Appraisal -.30 Challenge +.62 Avoidant + .51 Resources + 45.13

More useful for comparing the relative importance of predictors are the standardized weights, labeled as Beta on the output. The prediction equation for the standardized variables is as follows:

ZPredicted Perceived Stress = -.15 ZSelf-Efficacy -.19 ZAppraisal -.09 ZChallenge + .28 ZAvoidant + .18 ZResources

The sample multiple correlation coefficient was .53, indicating that approximately 28% of the variance in perceived stress in this sample can be accounted for by the linear combination of the 5 predictor variables. Thus, 72% of the variance in perceived stress is not accounted for by any of the predictor variables…. [read more]

#### U8D1 Regression Discontinuity and Correlation Research Term Paper…

… U8D1 Regression Discontinuity and Correlation Research Part I: (brief 1-2 paragraphs) Write about the logic of both the quasi-experimental regression discontinuity design and the non-experimental correlational approach and explain their limitations in permitting causal inferences. How are these two designs related?

Regression discontinuity is used when one cannot conduct an experimental study (randomized) and one can only go under the available data. In this case, both treatment and control group must be conducted under available data. What happens in this case is that participants with values of the observed variable above a certain threshold are assigned to the treatment group, and those with values below are assigned to the control group. They are no assigned randomly, but the measure underlying their assignment is observed instead.…. [read more]

#### Correlation and Regression Research Proposal…

… ¶ … correlation and regression using an example from public health to illustrate.

"Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another," such as the effect upon a student population's BMI of stocking vending machines with healthier food vs. allowing the vending machines to offer 'junk food' (Sykes 2010). Will the presence of healthier food in vending machines result in a lower average BMI in the population or will it not (because children are bringing junk food from home or other sources)? Correlation merely suggests that two factors tend to occur together -- for example, schools with a higher average student BMI may tend to have more…. [read more]

#### China: Inflation and Unemployment Correlation Data Analysis Chapter…

… 9 / -5.26 Oct 1.8 / -5.26 Nov1.8 / -5.26 Dec 1.8 / -5.26

1986

2

11.11%

Jan 2.2 / 11.1 Feb 2 / 11.2 Mar 2 / 11.1 Apr 2 / 11.1 May 2 / 11.2 Jun 2 / 11.1 Jul 2 / 11.1 Aug 2.2 / 11.1 Sept 2 / 11.1 Oct 2.1 / 11.1 Nov 2 / 11.1 Dec 2 / 11.1

1987

2

Jan 2.2/0.0 Feb2/0.0 Mar 2.1/0.0 Apr 2.2/0.0 May 2/0.0 Jun 2.2/0.0 Jul 2.1/0.0 Aug 2/0.0 Sept 2.1/0.0 Oct 2/0.0 Nov 2/0.0 Dec 2/0.0

1988

2

Jan 2/0.0 Feb2/0.0 Mar 2.1/0.0 Apr 2/0.0 May 2.1/0.0 Jun 2.2/0.0 Jul 2/0.0 Aug 2/0.0 Sept 2/0.0 Oct 2/0.0 Nov2/0.0 Dec 2/0.0

1989

2.6

Jan 2.6/3 Feb 2.6/3 Mar 2.6/3.2 Apr 2.6/3 May…. [read more]

#### SPSS Data Analysis Research Paper…

… However, to determine the strength of this relationship a Pearson's product-moment correlation coefficient (r) can be calculated for these two variables. Based on the SPSS results, there is a very strong, statistically significant correlation between hours and scores [r (18) = .967, p < .01, two-tailed]. The percentage of the variation in the dependent variable due to the independent variable is also very high (r2 = .934), which suggests that the average number of hours per week studied may be responsible for 93.4% of the final exam grade; however, a correlation cannot determine causality, only that there is a strong association between the two variables.

There are a number of potential ethical considerations concerning how the data was collected in this study. Of primary concern…. [read more]

#### Mathematics Analysis Techniques: Correlation Term Paper…

… B.

The direction of causality is that earning more money makes you spend more on your car, not that having a more expensive car makes you earn more money. Therefore, amount spent on car is the dependant variable (Y), and annual income level is the independent variable (X).

C.

Data:

Annual Income Level (\$)

Amount Spent on Car (\$)

Regression Calculation:

Regression Output:

Constant

-3996.77843523997

Std Err of Y Est

Squared

No. Of Observations

Degrees of Freedom

Coefficient(s)

Std Err of Coef.

Regression coefficient = 0.67

The regression coefficient of 0.67 shows that there is a positive correlation between annual income level and amount spent on car. This is as was expected. The graph also illustrates the general trend of increasing income level resulting in…. [read more]

#### Use of Vacation Hours Term Paper…

… Regression (for HR)

A look at the problem

ABC Corp. (an alias) believes it has a problem with the way employees are using vacation time. It seems that employees are accruing large amounts of vacation time and then not using that time or losing it because of company rules regarding how much time an employee can carry. ABC wants to test, through regression analysis, whether this is a legitimate, company-wide problem or whether it is only occurring in isolated cases.

Before we can conduct a regression analysis, it is important to first understand how ABC employees accrue vacation time. When employees first begin their employment at ABC, they accrue vacation time at a rate of two weeks (10 days, or 80 hours) per year. Employees…. [read more]

#### Linear Regression Models (Meier, Chapter 18 Article Review…

… Linear Regression models (Meier, Chapter 18 / 19)

These are used in order to determine whether a correlation (or relationship) exists between one element and another and, if so, in which direction (negative or positive).

The two variables are plotted on a graph. Independent variable on the x line (horizon); y- variable (dependent) on the vertical line. The pattern between them is called the 'slope'. The point where X and Y intersect online is called 'intercept'.

The theorem used tells us that the slope of the line will be equal to the change in x (IV) given changes of y (DV). The shape of the slope (their direction and gradient) describes the relationship between X and Y.

Linear regression, as are the previous models, is…. [read more]

#### Communicating the Results Term Paper…

… When data is presented in a manner that "invites extrapolation" this is not feasible for a group of scholars to attempt to comprehend and distortion occurs.

III. Multiple Regression Analysis Model and Analysis

The XYZ corporation is opening a new retail sales outlet and want to understand the best way to staff the stores with employees that will be best at selling their products. The staff sales are studied at stores already existing to examine the intelligence and outgoingness of employees so as to predict the sales performance of employees currently employed by the company. Three scores will be established for each salesperson including:

(1) Intelligence -- score on a scale of 50 (low intelligence) to 150 (high intelligence)

(2) Extroversion - score on a…. [read more]

#### Calculate a Mutiple Regression Problem A2 Coursework…

… ¶ … calculate a mutiple regression problem. All that you need are some variables and observations. Your assignment is to calculate and interpret a three-variable regression model, having a single dependent variable and two indepedent varaibles.

Y = a + b1X1 + b2X2

You will be predicting the percentage of vote for the incumbent president as the dependent variable and his popularity and the unemployment rate as indepedent variables. The data is provided below.

Vote Share Popularity Unemployment

Go to the site below and CAREFULLY READ the DIRECTIONS. Plug your data into the sheet, filling out columns X1 (popularity) and X2 (unemployment) for the independent variables and Y (vote share) for the dependent variable. Click "Calculate" at the top of the table. This will give…. [read more]

#### Run Linear-Linear and Log-Log Regression Models Term Paper…

… Run linear-linear and log-Log regression models using the data. Determine which regression model you would use. Why?

The most suitable regression model to use is the liner-liner regression model. This is because it is the most suitable model for the relationships between the variables and it is the model that yields an almost straight-line relationship on the graph plots. The reason as to why the log-log regression cannot even be used in this work is because some independent variables like Display and Feature have zero (0) values and it is not possible to compute the log value of 0 (zero).It therefore becomes automatic that the moist suitable model to use is the linear-linear regression model which yield the following graph and correlation coefficients.

#### Multiple Liner Regression Term Paper…

… All determinants were taken into account.

The dependent variable in this case was considered to be time while the independent variable was the number of newspapers delivered. The empirical relationship that was derived was of the nature:

Y=a+b*X+C

Where

The Y variable denoted Time while the X and C. denoted the distance traveled.

Data Description

The data collected included the time of delivery of the newspapers and the number of newspapers delivered. It was also important to consider the distance traveled in the delivery period of the particular newspapers. Several other micro determinants such as the demographics of the delivery zones were also noted but were not used in the hypothesis formulation. The table below shows the details of the data that was recorded during…. [read more]

#### Correlation and Regression Data Analysis Chapter…

… SPSS Statistics: Correlation & Regression

Correlation & Regression

Is there a relationship between defect rate and volume? If so, is it positive or negative?

Yes, there is a relationship between defect rate and volume. The relationship is positive, such that as volume increases, so does the defect rate (.740).

Which variable is the independent and which is the dependent variable?

The independent variable (predictor) is the volume of production, and the dependent variable is the defect rate (outcome).

Write out the regression equation and sketch it on the plot.

Predicted score = Bslope X + Bconstant

Predicted score = 0.027(X) + (-97.073)

Based on a review of the plot provided, and examining two points -- 4400 and 4000, which respectively appear to hit the Y…. [read more]

#### General Psychology Term Paper…

… ¶ … correlation does not prove causation.

According to Hersen and Thomas (2003), "Correlation does not prove causation'" is a statement every aspiring psychologist should learn. "A study may find a negative correlation between depression and self-esteem such that people with lower self-esteem are found to report higher levels of depression," they advise, and, "The temptation is to conclude that people are depressed because they have low self-esteem (and that by raising self-esteem, depression will be reduced)" (Hersen and Thomas 15). Researchers, though, must not succumb to this temptation to jump to a conclusion because there is nothing in the data that supports such a causal inference. In many cases, it is reasonable to assume that something is caused by something else, but appearances can…. [read more]

#### Multivariate Analysis of Parametric Data Essay…

… Maddox's research design involved a qualitative analysis of consumer behavior in regard to their usage of Web sites for the purpose of gathering prescription drug information as well as product requests. The research design involved the use of a questionnaire to measure the research questions. The questionnaire was placed online on a website belonging to one of the major pharmaceutical firms for a 2-week period., the responses were rated on a five-point (Likert) scale that ranged from 1 for very unimportant or unlikely to 5 for very important or likely.

Do you agree with the following statement?

Maddox (1999) should have used a Repeated Measure ANOVA to test for differences (based on AGE and GENDER) in the set of responses.

No.It would have been better…. [read more]

#### Parent Connect Zangle Term Paper…

… Zangle or Parent Connect is a system that allows the parent to check on the status of their child's grades as well as what is due and when an assignment is due. The purpose is to increase parental involvement and to reap the benefits that have long been assumed for increased parental involvement. This software is in use in several school districts today and is being considered by many more. What is needed is an examination of the software, what it does and how, and an assessment of how effective it has been in those situations where it is being used. Thus, a research project is to be designed to gather that sort of data and to set standards by which the software can be…. [read more]

#### Leadership Effectiveness Inventory Data Reliability Essay…

… 75

Managing Resources

2

3.27

0.88

0.66

Communication

1

3.73

0.65

0.66

Personal Example

3

3.66

0.71

0.77

In Table 4 above, two of the scales; Managing Resources and Communication have reliabilities slightly lower than the recommended alpha level of 0.7 though, not to the point of unacceptability. The other remaining seven scales are reliable. Even though there were statistically significant differences between the means of the four reporting groups on all the scales except communication, they had no systematic tendencies. Additionally, there was no consistent pattern of significant differences shown in the post-hoc tests. Therefore, it can be ascertained that when reliabilities are calculated separately for the four different reporting groups, the alpha coefficients are rarely more than 0.02 to 0.05 different from the…. [read more]

#### Statistics Regression Term Paper…

… Statistical Analysis and Forecasting of Housing Prices

The property chosen for predicting its selling price using statistical analysis techniques including multiple regression, correlation analysis, and factor analysis is shown to the right. The property is located at 2834 N. Danbury Street in Orange, California.

The home has 4 bedrooms and 3 baths, and is located on a cul-de-sec street, which is ideal for children. The home is 2,240 square feet on a lot of 7,000 square feet, and was built in 1995. The most recent owners completely re-vamped the kitchen and downstairs floors, investing well over \$40,000 in both projects. The downstairs bedroom has been converted to a home office and the upstairs master bedroom, custom children's bedroom and third bedroom are ideal for a…. [read more]

#### Prediction Essay…

… 38; median = 3.3; mode = N/A (all values at the same frequency); variance = .692889; standard deviation = .263228; kurtosis = -1.2189; skew = .182109; range = 2.5; sum = 38.3.

Higher reaction time memory score group: being = 9.24; median = 8.85; mode = 9.5; variance = 5.004889; standard deviation = 2.237161; kurtosis = 6.86883; skew = 2.443916; range = 7.9; sum = 94.2.

Aside from the obvious difference is one would expect when separating groups into low and high scores (e.g., higher/lower mean, median compared to the overall group results, different sum, etc.) there are a couple of interesting differences here. First, the lower reaction time group does not have a specific mode (although scores in the distribution at the same frequency…. [read more]

#### Economic and Quantitative Analysis Topics Term Paper…

… The United States has shifted in a major way on two different occasions over its existence. First, they went from an agrarian economy to an industrial one and now they are shifting to a service/knowledge foundation while the once dominant manufacturing industry in that country is becoming an afterthought except when speaking of advance/high-tech manufacturing. If someone were to analyze the United States economy over its entire existence, using a singular and fixed model would be asinine and obtuse because of these seismic changes that have occurred. Looking at the United States economy now through a year 1840 lens would be silly.

In a similar vein, looking at Nigeria now would require a certain model and that model would exhibit certain patterns and trends. However,…. [read more]

#### Prediction African-American Parents Research Proposal…

… ¶ … Prediction African-American Parents' and Guardians' Satisfaction With Teachers and Public Schools," Thompson (2003) points out the shortcomings of public school reforms such as the 2002 No Child Left Behind Act. The researcher notes that most disadvantaged students and especially African-American students have been systematically left behind. The Act, although well intentioned, fails to address the need for improved parent-teacher relations. Regression analysis shows that parental satisfaction as early as elementary school is one of the strongest predictors of parental satisfaction with their children's overall education in American public schools. Thompson's research demonstrates the failure of public policy reforms and suggests that future policy be directed at improving relations between school administrators and African-American parents.

#### Psychology Research Methods Imagine Studying Essay…

… Or -1 to 0. However, the best option is to have it in between -1 and +1 to determine the strength of the relationship when determining if a correlation exists. When a non-linear relationship occurs, a person will not see a relationship either negative or positive when drawing a regression line through the data from a scatter plot. For someone to see if the size and sign will increase or decrease as a result and a person can work around this issue in a couple of ways. He or she can choose to use a goodness of fit with the current data. The other option is having one variable split into a number of segments, and then an individual can do an analysis of variance…. [read more]

#### Perception and Performance: What Derivatives Hedging Methodology Chapter…

… Investors Perceptions of Derivatives Strategy

The proposed research will explore the perceptions of investors toward the use of derivatives and their assessment of managers who employ the use of derivatives. The review of the literature on how investors perceive derivatives provided a broad swath of research and an international perspective. The area of behavioral finance is of particular relevance to the proposed research, as several investigators have suggested. The matter of asymmetrical information -- a classic consideration in investment -- emerged in a somewhat distinct form in the literature review. The knowledge level that investors have regarding derivatives appeared to play an important part in how managers who use derivatives are perceived, in investors' perceptions of the investment strategy, and in the ability of market…. [read more]

#### Multiple Regression Analysis Is an Essential Constituent Methodology Chapter…

… Multiple regression analysis is an essential constituent of marketing research procedures. Better usability over several kinds of data, the ability to handle failure of pre-defined assumptions and ease of understanding are some its advantages. Predicting research results and analyzing the different variables used in those predictions are the two primary objectives of multiple regression analysis. In a situation where a couple or more of such predictor variables are related to each other, the results can be erroneous. The problems are usually with regards to summarizing results based on the coefficient size, the related t-tests and typical errors. There are several ways to deal with this issue, termed as multicollinearity. The effect of collinearity cannot be interpreted in seclusion. There needs to be other factors to…. [read more]

#### Data Analysis for Datasets Data Analysis Chapter…

… 64,

p < .05,

R2 = .44,

However, the results show that phobia does not contribute significantly in predicting scores of stat quiz because t (19) = -2.74, not significant, and Beta = -.28. On the other hand, the previous math courses can significantly predict scores because t (19) = 2.27, Beta = .23, and p < .05.

2.Repeating the First Exercise

1. SPSS output:

Descriptive Statistics

Mean

Std. Deviation

N

Stat quiz

6.85

1.736

85

Phobia

3.25

2.492

85

Prevmath

1.41

1.284

85

Mathquiz

29.07

9.480

85

Correlations

Statquiz

Phobia

Prevmath

Mathquiz

Pearson Correlation

Statquiz

1.000

-.357

.365

.508

Phobia

-.357

1.000

-.467

-.282

Prevmath

.365

-.467

1.000

.419

Mathquiz

.508

-.282

.419

1.000

Sig. (1-tailed)

Statquiz

.000

.000