Exercise 31 Essay example

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

Ratio analysis are useful tools when judging the performance of a company by weighing and evaluating the operating performance (Block-Hirt). There are 13 significant ratios that can separate by four main categories,

In this study, t= -3.15 describes the mental health variable. It is significant because they are the variables being tested since the p value is 0.002 and the alpha is 0.05, the difference can cause the null hypothesis to be rejected.

Reporting t values- when you have already reported p values- is not necessary and can cause confusion.

Note: the program we used on this worksheet said the results were not significant, but then in statistical notion it had “p < 0.05”. That is confusing because I believe it should be “p >

We can use z-table instead of t-table because the standard deviation comes from the population.

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

One-sample t-test are used in the parametric test which analyzes the means of populations. The t-test for independent groups are statistics that relates difference between treatment means to the amount of variability expected between any two samples of data within the same population (Hansen & Myers, 2012). Critical values are used in significant testing provide a range of t distribution that is used in whether a null hypothesis is rejected. Based on the data below as the level of significance is at .05, thus the critical values would fall under ±1.860 and the t value for this is 1.871 would suggest for the null to be rejected as it is greater than the critical value (Privitera, 2015, p. 267). Based on the population mean of 70 there was a mean difference of

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

According to the above results from MINITAB, the p-value of 0.038 is smaller than the significance level of 0.05; consequently, the null hypothesis will be rejected. There is sufficient evidence to support the

You will use a 5% level of significance to conduct this test. You get an asymptotic t-stat equal to

It tells that the t-statistic with 97 degrees of freedom was 2.14, and the corresponding p-value was less than .05, specifically around 0.035. Therefore, it is appropriate to conclude the research study was statistically significant.

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

Health promotion aims a wide range of targets at different levels in the community. The huge benefits resulted from health promotion and disease prevention efforts make many realize that this domain of health care is worth to be invested in. The focus is shifted evermore from the medical to the preventative approach, from treating the disease to avoiding the onset of it.

As each subject has different work styles and habits coupled with a diverse psychological perception towards the nature of their work, it is necessary to determine whether their responses to the health promotion program will bring about the same results or not.