Hypothesis: The local car dealership that offers in house financing has a large percentage of accounts that are past due because of the lenient credit profile they utilize to approve financing. A hypothesis is a statement that variables are assigned to the case. (Cooper & Schindler, 2011) In the above hypothesis the case is accounts past due and the variable is the lenient credit profiles that are utilized for approving finance. My hypothesis has adequacy for its purpose, is testable. I believe my hypothesis needs to be strengthened to be better than its rivals. When utilizing the chart on page 64 of our text I do find several areas for improvement. After going through the checklist I believe that a better hypothesis would be made after an initial analysis of the percentage of late payments and the minimum credit scores. A hypothesis could then be created that is more specific and testable as well as analyzed against other statistics such as the percentage of late payments for a higher, specified credit score. If research shows 70% of customers that have late payments have a credit score below 600 a stronger hypothesis would state: If the local car dealership that offers in house financing requires a credit score higher than 600 for approved financing the percentage of late payments they receive will be less than 70%. Adequacy for its purpose| |

| |
*Does the hypothesis reveal the original problem condition?| Yes, the problem is a large amount of accounts that are past due.| | |
*Does the hypothesis clearly identify facts that are relevant | It clearly states facts regarding financing approval but does not | and those that are not?| include any other facts, relevant or not.| | |

*Does the hypothesis clearly state the condition, size, or| It only states that lenient profiles are used, this should be| distribution of some variable in terms of values meaningful| made stronger by listing a specific minimum credit score| to...

...What is Hypothesis Testing?
A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true. Hypothesis testing refers to the formal procedures used by statisticians to accept or reject statistical hypotheses.
Statistical Hypotheses
Null hypothesis. The null hypothesis, denoted by H0, is usually the hypothesis that sample observations result purely from chance.
Alternative hypothesis. The alternative hypothesis, denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some non-random cause.
Hypothesis Tests
Statisticians follow a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesis testing, consists of four steps.
State the hypotheses. This involves stating the null and alternative hypotheses. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false.
Formulate an analysis plan. The analysis plan describes how to use sample data to evaluate the null hypothesis. The evaluation often focuses around a single test statistic.
Analyze sample data. Find the value of the test statistic (mean score, proportion, t-score, z-score, etc.) described in the analysis plan.
Interpret...

...Chapter-11
Testing of Hypothesis:
(Non-parametric Tests)
Chapter-11: Testing of Hypothesis - (Non-parametric Tests)
2
11.1. Chi - square ( χ )Test / Distribution
2
11.1.1. Meaning of Chi - square ( χ )Test
2
11.1.2. Characteristics of Chi - square ( χ )Test
2
11.2. Types of Chi - square ( χ )Test / Distribution
2
11.2.1. Chi - square ( χ )Test for Population Variance
2
11.2.2. Chi - square ( χ )Test for Goodness-of-Fit
2
11.2.3. Chi - square ( χ )Test or Independence
11.3. Analysis of Variance (ANOVA)
11.3.1. Meaning of ANOVA
11.3.2. ANOVA Approach
11.4. ANOVA Technique
11.4.1. One-way ANOVA
11.4.2. Two-way ANOVA
11.4.3. ANOVA in Latin-square Design
11.5. Other Nonparametric Techniques
Summary:
Key Terms:
Questions:
11.1. CHI-SQUARE (
) TEST /DISTRIBUTION
2
11.1.1. Meaning of Chi - square ( χ )Test
2
A chi-square test (also chi squared test or χ test) is any statistical hypothesis test in which the
sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true,
or any in which this is asymptotically true, meaning that the sampling distribution (if the null
hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by
making the sample size large enough. The Chi-Square (
) test is the most popular non-parametric
test/methods, to test the hypothesis. The...

...
The word ‘Hypothesis is derived from a Greek word, which means ‘to suppose’. It is usually considered as the principal instrument in research. For a researcher it is a formal question that he or she intends to resolve. In this way a hypothesis may be defined as a proposition or a supposition. The main function of hypothesis is to guide the collection and processing of materials and direct the research. Hypothesis is a tentative conclusion. It is facts based theory. A research scholar will analyze the information from variety of sources in order to create a hypothesis. Hypothesis can be of two types –
- Explanatory
- Descriptive
In explanatory hypothesis researcher tries to account for a given fact and the explanation is provisional because it is based on inconclusive proof. This method is especially used in finding out laws in history. The defeat of the army of Siraj in the battle of Plassey is a fact. Various explanations are offered for this fact. Again the rise of Gandhi has been accounted for variously by various authors such as Shaid Amin and Judith Brown. These explanations are really the nature of hypothesis.
Descriptive hypothesis is employed for making a complex mass of facts, which are isolated from one another, a meaningful unit by describing it in a collective manner. To...

...WHAT THE H2!?
09-10-13
This is journal entry #20 10:30 AM 8/10/2050: The riots are still raging in the streets and gas prices are $200 a gallon. It has been 15 days since we have been told that there is no more fossil fuels. Our civilization is at the tipping point; like I said there is violent riots in the streets the lights are not turning on and neither is the water. My mom tells me it’s like this because when she was a kid everyone abandoned the idea of “clean energy” because they were told that scientists figured out how to clean up the pollution. No one thought of when we were going to run out fossil fuel and now we have. We need a new source of power, a clean and unlimited reserve of energy, hydrogen.
Hydrogen is a clean and unlimited resource. Hydrogen is a completely clean resource, when used in cars it’s only emission is water vapor and heat. Hydrogen is virtually unlimited when hydrogen runs out our universe will have ended ( most likely one hundred trillion years) 100,000,000,000,000 years from now.
Hydrogen is extremely efficient, it is 3 times more efficient than gasoline. You can get 480 miles a tank with the Toyota FCHV. From coral you can make it all the way to San Diego, CA and go another 156 mi on one tank.
Some argue that hydrogen is very flammable and can explode. Those that argue this are 100% correct but it is not any more flammable than the gasoline in your car right now.
Hydrogen is only $1.80 gallon and 3 times more...

...Why We Don’t “Accept” the Null Hypothesis
by Keith M. Bower, M.S. and James A. Colton, M.S.
Reprinted with permission from the American Society for Quality
When performing statistical hypothesis tests such as a one-sample t-test or the AndersonDarling test for normality, an investigator will either reject or fail to reject the null
hypothesis, based upon sampled data. Frequently, results in Six Sigma projects contain
the verbiage “accept the null hypothesis,” which implies that the null hypothesis has been
proven true. This article discusses why such a practice is incorrect, and why this issue is
more than a matter of semantics.
Overview of Hypothesis Testing
In a statistical hypothesis test, two hypotheses are evaluated: the null (H0) and the
alternative (H1). The null hypothesis is assumed true until proven otherwise. If the
weight of evidence leads us to believe that the null hypothesis is highly unlikely (based
upon probability theory), then we have a statistical basis upon which we may reject the
null hypothesis.
A common misconception is that statistical hypothesis tests are designed to select the
more likely of two hypotheses. Rather, a test will stay with the null hypothesis until
enough evidence (data) appears to support the alternative.
The amount of evidence required to “prove”...

...all, the video did a fair job buttressing my understanding of hypothesis testing. The textbook explained the aspects and steps of hypothesis testing in a legible fashion, while the video helped demonstrate a real-life application.
I learned from the text that hypothesis testing is a “Procedure for deciding whether the outcome of a study (results from a sample) supports a particular theory or practical innovation (which is thought to apply to a population)” (Aron A., Aron, E., and Coups, 2011, p. 145). I also learned that hypothesis testing follows a set procedure that appears as follows:
Step 1) Restate the question as a research hypothesis and a null hypothesis about the populations
- Basically, a researcher constructs a hypothesis. Then he/she forms a null hypothesis that opposes the research hypothesis in
polar fashion. To help support one’s research hypothesis, one has to disprove the null hypothesis.
Step 2) Determine the characteristics of the comparison distribution
- When using two or more samples, one must gather information about the distribution of means.
Step 3) Determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected
- Most researchers choose a significance level of 0.05 or 0.01.
Step 4)...

...APP6JMaloney problems 2. 4, 6, 10, 18, 22, 24
2 ) The value of the z score un a hypothesis test is influenced by a variety of factors.
Assuming that all the other variables are held constant, explain how the value
of Z is influenced by each of the following?
Z= M - u / SD
a) Increasing the difference between the sample mean and the original.
The z score represents the distance of each X or score from the mean.
If the distance between the sample mean and the population mean the z score will
increase.
b) Increasing the population standard deviation.
The standard deviation is the factor that is used to divide by in the equation. the bigger the SD,
then the smaller the z score.
c) Increasing the number of scores in the sample.
Should bring the samples mean closer to the population mean so z score will get smaller.
4) If the alpha level is changed from .05 to .01
a) what happens to the boundaries for the critical region?
It reduces the power of the test to prove the hypothesis.
You increase the chance of rejecting a true H
b) what happens to the probability of a type 1 error?
Type 1 error is falsely reporting a hypothesis,
Where you increase the chance that you will reject a true null hypothesis.
6) A researcher is investigating the effectiveness of a new study skills training program for elementary
school childreen. A sample of n=25 third grade...

...A hypothesis is a claim
Population mean
The mean monthly cell phone bill in this city is μ = $42
Population proportion
Example: The proportion of adults in this city with cell phones is π = 0.68
States the claim or assertion to be tested
Is always about a population parameter, not about a sample statistic
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
If the sample mean is close to the stated population mean, the null hypothesis is not rejected.
If the sample mean is far from the stated population mean, the null hypothesis...

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