Data Analysis Notes

Hypothesis testing and development provides a baseline for taking ideas or theories that were initially created by another person in regards to the markets, economy, or investing and then determining if the

The goal of inferential statistics is to end up rejecting the null hypothesis and concluding that a significant relationship exists; therefore, the null hypothesis always presume no relationship.

Null hypothesis. The null hypothesis, denoted by H0, is usually the hypothesis that sample observations result purely from chance. In this case, according to null hypothesis there is no significant difference between business students at Rocky University who were involved in cheating and business students elsewhere.

A population is the entire group to be studied and a sample is a portion of the population.

Step 1: During this step of hypothesis testing, the query is stated again as a research theory and a null theory regarding the populations. The null and research hypothesizes are the opposites of each other. This step is necessary because it explains the theory and recognizes the populations, which will be worked throughout the study.

Null hypothesis states that all population means are equal. The alternative hypothesis show that at minimum 1 population mean is various.

The null hypothesis consists of all independent variables that have a regression coefficient which is equal to zero; the alternative hypothesis is at least one independent variable that has a regression coefficient which is not zero.

The central limit theorum states that the average of a distribution will be normal regardless of distribution unless the parent distribution is missing data. Simple random sampling is a form of sampling where the total sample group consists of N objects and the sample taken consists of n objects resulting in a predictable result. The sample mean of a random sampling would be within the resulting confines of the sample group. In a population of 20 people, there were 15 donars, but the average gift was well below the per-capita income. However, there were a minority of large donors that far exceeded the per-capita income causing the mean to be much higher than expected.

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

A sample can include any object or characteristic in a population. It is necessary to use samples for research, because it is impractical to study the whole population. The author is asking the student; How can we make inferences about whole populations from samples drawn from the population? By inferential statistics.

Another randomly selected number from 1 to 25 is chosen to be the interval value n. Each sample contains the first member and each subsequent nth survey until the sample size is reached. This method is simple; each survey response is given an equal probability of being selected, and the whole population of surveys will be evenly sampled.

"Inferential statistics is drawing conclusions of large data sets called a population" (Jaggia & Kelly, 2014). To analyze inferential statistics, I use regression analysis. "Regression analysis is used to examine the relationship between two or more variables" (Jaggia & Kelly, 2014).

-The sampling distribution is a distribution of the results if all possible samples are selected, where as a population distribution is the distribution of a particular variable of interest.

An important property of a test statistic is that we must be able to determine its sampling distribution under the null hypothesis, which allows us to calculate p-values.

As discussed in the Module 5 DQ 1, the most vital function of the hypothesis testing is in researches where the needs to be a conclusion drawn from a logical approach of making a claim and proving that the claim is rejected or not with respect to samples and respective statistical approaches. The claim could be for the effect on blood pressure with certain hypertensive drugs as Beta-blockers, the adverse effects of certain anti-cancer drug in comparison to another anti- cancer drug, the claim that fast foods cause heart diseases in contrast to healthy, the claim that alcohol and drunk driving are the major cause of accidents, etc. The hypothesis testing is taken from a normally distributed population with the known mean and known or unknown standard deviations; the claim could also be conducted about the standard deviations or variances. It is normally concerned with drawing sensible inferences and are not associated with making prediction from the values drawn from the variables.