Regression and Hypothesis Testing

So, we should reject the null hypothesis H0. At a 0.05 level of significance level, we conclude that there is a significant difference between the average height for females and the average height for the males.

 Inferential statistics helps us to analyze predictions, inferences, or samples about a specific population from the observations that they make. “With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone” (Trochim, 2006). The goal for this type of data is to review the sample data to be able to infer what the test group may think. It does this by making judgment of the chance that a difference that is observed between the groups is indeed one that can be counted on that could have otherwise happened by coincidence. In order to help solve the issue of generalization, tests of significance are used. For example, a chi-square test or T-test provides a person with the probability that the analysis’ sample results may or may not represent the respective population. In other words, the tests of significance provides us the likelihood of how the analysis results might have happened by chance in a scenario that a relationship may not exist between the variables in regards to the population that is being studied.

Cohen’s paper The Earth is Round (p>0.05) is a critique of null-hypothesis significance testing (NHST). In his article, Cohen presents his arguments about what is wrong with NHST and suggests ways in which researchers can improve their research, as well as the way they report their research. Cohen’s main point is that researchers who use NHST often misinterpret the meaning of p-values and what can be concluded from them (Cohen, 1994). Cohen also shows that the NHST is close to worthless. NHST is a way to show how unlikely a result would be if the null hypothesis were true. A Type I error is where the researcher incorrectly rejects a true null hypothesis and a Type II error is where the researcher incorrectly accepts the false null

Hypothesis is typically used in quantitative research only. Moreover, when a question poses an inquiry on the relationship between two variables, a hypothesis is a statement declarative in nature of the relationship between different variables (Pajares 2007). A researcher chooses whether to use a question or a hypothesis depending on the purpose of the research, its objectives, the methodology for the research and the preference of the audience to receive the research. A researcher must be able to interpret the final outcome with reference to the research questions or the hypothesis used (Pajares 2007). A research requires a minimum of two hypotheses namely a null and an alternative hypothesis.

Testing allows the p-value that represents the probability showing that results are unlikely to occur by chance. A p-value of 5% or lower is statistically significant. The p value helps in minimizing Type I or Type II errors in the dataset that can often occur when the p value is more than the significance level. The p value can help in stopping the positive and negative correlation between the dataset to reject the null hypothesis and to determine if there is statistical significance in the hypothesis. Understanding the p value is very important in helping researchers to determine the significance of the effect of their experiment and variables for other researchers

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.

The null hypothesis that there is no relationship between the amount of coffee consumption and GPA (p = .62).

* Correlation coefficient (R-squared) – This represents how well the independent variables (X) explain the response variable (Y).

In an experimental research, the use of a research question answers the thesis statement that enables one to research about a problem (Yin, 2013). From the experiment, the research question can be clearly stated as “which vaccine is more effective for preventing getting the flu”. In this case, the problem being researched about is the flu the possible solutions to this problem which the use of vaccines is being analyzed. The null hypothesis of this experiment states that there is no effective vaccine for preventing getting the flu while the alternative hypothesis states that there is an effective vaccine for preventing getting the flu.

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.

 Explain how the data collected will provide the data necessary to support or negate the hypothesis or proposition

“Hypothesis testing is a decision-making process for evaluating claims about a population” (Bluman, 2013, p. 398). This process is used to determine if you will accept or reject the hypothesis. The claim is that the bottles contain less than 16 ounces. The null hypothesis is the soda bottles contain 16 ounces. The alternative hypothesis is the bottles contain less than 16 ounces. The significance level will be 0.05. The test method to be used is a t-score. The test statistic is calculated to be -11.24666539 and the P-value is 1.0.  The P-value is the probability of observing a sample statistic as extreme as the test statistic, assuming the null hypothesis is true. The T Crit value is 1.69912702. The calculations show there is enough evidence to support the claim that the soda bottles do

"Correlation is a measure of association that tests whether a relationship exists between two variables. It indicates both the strength of the association and its direction (direct or inverse). The Pearson product-moment correlation coefficient, written as r, can describe a linear relationship between two variables" Correlation (n.d). As a human service professional and completing research there are advantages and disadvantages to correlational research methods, such as using correlational research it allows us to collect data and determine the strength and direction of what it is we

Gravetter and Wallnau (2015) contend that a close relationship exists between hypothesis testing and the use of confidence intervals. For example, all estimates in the range are credible values for the assessing of parameters when a confidence interval of 95% is constructed (Gordon, 2010; Gravetter & Wallnau, 2015; Howell, 2014). In comparison, values outside the interval are rejected and are considered improbable. Howell (2014) contends that if the null hypothesis includes the confidence interval within it, the null hypothesis will not be rejected; however, the null hypothesis will be rejected if the value of the parameter is outside the confidence interval. Bonett and Wright (2014) argue that when planning a multiple regression analysis, it is essential to attain a population size that will support the study and provide narrow confidence intervals. Therefore, the confidence level in this study was constructed at 1-alpha or 95% interval, meaning that the null hypothesis could be rejected at the 0.05

Hypothesis testing plays an important role in these testing approaches because it forces organizations to think about the current state of their business and to make claims about their customers preferences and desires. The organization then is forced to initiate different projects to investigate and determine whether these claims are true on not. As Avinash explained, starting with a hypothesis allow organization to accomplish two important things; first it allow for the contribution of clear input into test script creation and second, it helps with the establishment of clear success measurement (Avinash, Kaushik, Web