Do caffeine drinkers have higher resting heart rate compared with non-caffeine drinkers?

To measuring the comparison of the two variables t-test is applied because the means can be easily compared among the two unrelated groups for the continuous same independent variables. In the independent t sample t-test the dependent variables which is “higher resting heart rate” is measured on the continuous scale meanwhile the independent variable that is “caffeine drinkers” it consist of two categorical independence groups as caffeine drinkers or not drinkers. This independent variable is meat each of the criteria as drinkers or not drinkers. Independent sample T-test has been applied by using SPSS and the output generated in such manners for both of these variables as caffeine drinkers and heart rate.

Interpretation

The above given table is represents the group Statistics and in the first column of this table the two variables are illustrating under the word caffeine  for the two kinds on the people one are those who are caffeine drinkers and 2^nd are those who are not caffeine drinkers.  Few of the descriptive statics are also represent n this table as the Column N shows the numbers of caffeine drinkers there are only 90 peoples who are caffeine drinkers but remaining 139 are not caffeine drinkers who have participated in this study. The mean value for the caffeine drinkers is 191.26 and the mean value for not drinker is193.7317.

Interpretation

The above given table is representing the value for the independent sample t-test Levene's Test for Equality of Variances has been applied to comparing the both of these variables as heart  rate and of caffeine drinkers or not drinkers.  In this table the value F statics is 11.502 that show the good fitness of the model because the value of the F is greater than 10. The significance level is less than 0.05 it shows theses variables highly significant relationship among each other. Sig. (2-tailed) is also less than 0.05 for both assumed and un assumed equal variances.

Category B

 Is BMI related to Waist circumference?

For measuring the relationship among BMI and Waist circumferences the Pearson correlation has been applied because its good technique to measuring the relationship between two variables and it shows accurate level of significance for the both of the variables.

Interpretation

The value of the Pearson correlation is represents in the above given table and the level of the significant value is 0.01 for 2-tailed significance. The values of the independent and dependent variables that are waist circumferences and BMI respectively are 0.643. This value is round about 1 that is the accurate measure for the correlation. For each matching the coefficients of person are assumed bi-varite in its nature. It is also referred as the best measure for the linear relationships.  The two stars **on the value of the Pearson correlation represent that there is the significant positive relationship between both of these variables. But in the most of researches and analysis the regression analysis is referred as the good measure for the linear association meanwhile the correlation is considered as the weak measure for the linear relationships. But in the given data of the BMI and waist circumferences its good match to illustrates how these both variables are BMI and waist circumferences are related to each other. It shows the direct relationship among both of these variables.

  The significance value is 0.000 for both variables that is less than 0.01. BMI and Waist circumferences are the correlated variables. It shows that the level of the BMI is related to the waist circumferences if the waist circumferences will be increase than the level of BMI will increase meanwhile if the waist circumferences decrease the level of the BMI will decrease automatically.  These both variables has positive significant relationship with each other’s and the data collected during this experiments shows that BMI is related  to waist circumferences of the participates who participated in this study.

Category C:

Is there change in Waist-to-hip ratio measurements taken two weeks apart by the same tester?

                The various analyses can be measure for this created situation as the one way ANOVA test has been applied on this situation that is the good module for measuring required association of both these variables.    The few quite tables are generated by using SPSS for the test of the one way ANOVA analysis. This part also represents the several notables that are required for understanding the various steps of the one way ANOVA.  This table can be discuss by discussing the assumptions of the such kinds of the tables as it includes relevant box plots, Homogeneity variances, ANOVA, descriptive tables and various kinds of the graphs. This test is applied because the one variable has scales level of measurement meanwhile another variable is categorical in nature and that is the ordinal which the exercise mode of the respondents.

Interpretation

The given provides descriptive table above some very useful descriptive statistics, including the mean, standard deviation and 95% confidence intervals for the dependent variable exercise mode for each separate group (strength, mixed, Endurance and power), and the total of the all group when all of these groups are combined. For describing the data the table is useful.

Interpretation

The above given table is representing the output for the ANOVA analysis along with the statistically significant difference among means of the groups. The significance value in the above given table is .196 that is the greater than 0.05 it shows low level significance among these variables. It is not mention that the one is the particular group differentiates and for the measuring differentiation of both variables several other test are applied. The ANOVA table is illustrating about the specific quantifiable framework that is required to assessing the potential difference as indicated by the scale subordinate factors just as the ostensible factors which have just two arrangements. Subsequent to directing the ANOVAs test, it has been seen that there is a statically huge distinction between the independent and continuous variables. In the above-given table, the F is 1.580 which is demonstrating that the model isn't solid match. In the above table P 0.196 which is more prominent than 0.05.

Category D

Is dominant handgrip strength affected by the mode of exercise typically performed?

The effect of the variable on another variable can easily measured by the regression analysis because the values of linear regression  shows the significance level and it generates major 3 tables. It is also applied to measure the affect of the independent variables on the dependent variables.  By applying the regression analysis there are three major tables derived which shows the various values for independent and dependent variable that is the mode of exercise and dominated hand grip. These tables are includes as the Model summary, Table of ANOVAs, Table coefficients.

Interpretation

In the above given table of the model summary the value of the R represents about the effect of Exercise Mode on Dominated hand grip. It shows the 11 % effect of Exercise Mode on Dominated hand grip that is very little effects. It means more practices can makes hand grips better.