Comparing Means in Spss

We conduct an independent sample t-test using Excel, and obtain the following output (see t-test-height)

2. Compute the means for the following set of scores saved as Ch. 2 Data Set 3 using IBM® SPSS® software. Print out a copy of the output. (Please refer to attachment)

Select one (1) project from your working or educational environment that you would use the hypothesis test technique. Next, propose the hypothesis structure (e.g., the null hypothesis, data collection process, confidence interval, test statistics, reject or not reject the decision, etc.) for the business process of the selected project. Provide a rationale for your response.

Topics Distribution of the sample mean. Central Limit Theorem. Confidence intervals for a population mean. Confidence intervals for a population proportion. Sample size for a given confidence level and margin of error (proportions). Poll articles. Hypotheses tests for a mean, and differences in means (independent and paired samples). Sample size and power of a test. Type I and Type II errors. You will be given a table of normal probabilities. You may wish to be familiar with the follow formulae and their application.

When you perform a test of hypothesis, you must always use the 4-step approach: i. S1:the “Null” and “Alternate” hypotheses, ii. S2: calculate value of the test statistic, iii. S3: the level of significance and the critical value of the statistic, iv. S4: your decision rule and the conclusion reached in not rejecting or rejecting the null hypothesis. When asked to calculate p–value, S5, relate the p-value to the level of significance in reaching your conclusion.

The t-test is a parametric analysis technique used to determine significant differences between the scores obtained from two groups. The t-test uses the standard deviation to estimate the standard error of the sampling distribution and examines the differences between the means of the two groups. Since the t-test is considered fairly easy to calculate, researchers often use it in determining differences between two groups. When interpreting the results of t-tests, the larger the calculated t ratio, in absolute value, the greater the difference between the two groups. The significance of a t ratio can be determined by comparison with the critical values in a

At one school, the average amount of time that tenth-graders spend watching television each week is 18.4 hours. The principal introduces a campaign to encourage the students to watch less television. One year later, the principal wants to perform a hypothesis test to determine whether the average amount of time spent watching television per week has decreased. Formulate the null and alternative hypotheses for the study described.

At the .01 significance level is there a difference in the mean amount purchased on an impulse at the two stores? Explain these results to a person who knows about the t test for a single sample but is unfamiliar with the t test for independent means.

We conduct an independent sample t-test using Excel, and obtain the following output (see sheet T-TEST)

Table~\ref{tab:example1} shows the coefficient for Batting Average which is 5.4, this mean that 1 unit of increase in batting average produce a 5.4 increase in balco scandal variable. If our measure of batting average is equal to zero, we would expect the efect of steroides (balco scandal) decrease in a 1.5\%. The t value calculated is greater than the critical value of t thus we can reject the null hypothesis in this case. The p value shows a statistical significance at 95\% of confident.

Conclusion : Reject the null hypothesis. The sample provide enough evidence to support the claim that mean is significantly different from 12 .

This paper will examine a data analysis and application for an independent t test comparing the mean GPAs of a sample of male and female students. It will pose a research question that the data will set out to answer. It will provide a null hypothesis and an alternative hypothesis, and will provide an analysis showing why the null hypothesis should be accepted or rejected in favor of the alternative hypothesis.

Statistical analysis Data were analyzed using IBM SPSS statistics 20.0 software. Independent t‑test was used for comparison of the continuous variable between the two groups. Variables expressed as mean ± standard deviation then converted to standard errors of the mean. For repeated or continuous measurements, analysis of variance was used. P < 0.05 was considered as statistically significant.

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.

SPSS is the premier statistical analysis software, and has been the industry benchmark for decades. It is practically impossible to do work in the social sciences without understanding the basic uses and functions of SPSS. As the full name of the software (Statistical Package for the Social Sciences) suggests, the suite is especially designed for use in the social sciences and has become standardized in some fields like psychology (Field, 2005). Researchers can use SPSS to input the raw data from their research designed and the software can compute a practically limitless set of statistics based on those raw figures and inputs. Basic descriptive statistics such as frequency and rates of distribution are obviously available, as are various ratios that can be drawn from the data. Simple correlations can therefore be drawn. However, there are many more robust uses for the software including the ability to run some of the most sophisticated analytic techniques that ensure the reliability and validity of the research. These techniques include an Analysis of Variance (ANOVA), bivariate correlations, t-tests, chi-tests and more. Regression analyses, factor analyses, and two-step cluster analyses are also possible using SPSS (IBM, 2013). It is impossible to imagine computing the data gleaned from research in any other way, although there are competing products on the market. The vast majority of researchers and analysts in the educational, "think tank," and corporate sectors are