...Board MGMT600-1501A-03 Null and Alternative Hypothesis Instructor: Professor Dr. Throop, R. February 2, 2015 Null and Alternative Hypothesis A senior executive at Company W is having an issue understanding the concept of null and alternative hypothesis in the snack food research. In this paper, I will make it understandable for him to be able to see how this hypothesis’s work. Though it may be a little cryptic, the concept of null and alternative hypothesis is not very difficult. A null hypothesis is the speculation of a declaration that researchers hope to try to nullify or disprove. The alternative hypothesis is what the researcher actually believes to be the truth about the declaration (M.U.S.E, 2015). A hypothesis involves two types of statements: the null and alternative hypothesis. Statistical implication starts by identifying that research questions can be stated in conditions of a choice between two clear and mutually options. The only reason that null hypothesis and alternative hypothesis are different is chance. Let me break this down so that it will be easier to understand. I will break it down in a formula based statement in regards to the sack food industry. Let us look at this: A researcher must present a statement that is able to be proved or disproved. Once the expectation is known, then a counter statement is provided, which also known as the “null” hypothesis. We can represent this by using the term “H0” which means: H is the hypothesis and 0 is there is no...
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...ar(x) = 10 and V ar(y) = 2.5. The covariance can then be calculated as Cov(x, y) = 5. Thus our correlation coefficient is 1. This means that the data = ”fully” linearly correlated, or that there is a function y = ax + b that translated the x data into the y data. 3. We want to check if on average a cup of butter contains 250 grams or less. A sample test of 25 cups reveals an average of 248.2 grams and a standard deviation of 2.5 grams. Check whether the smaller mean is a significant difference for a significance threshold of 5%. Or null hypothesis is H0 : µ = 250. We test this against H1 : µ < 250.√ Therefore our statistic √ ¯ is T = X−250 n. From the sample we get a t-value of T = 248.2−250 25 = −3.6. We reject S 2.5 the null hypothesis for too small values of T . Therefore P (T ≤ tα ; H0 ) = α = 0.05. Using the table we find P (T (24) ≥ 1.71) = P (T (24) ≤ −1.71) = 0.05, therefore tα = −1.71. Since our t-value is smaller than tα , we reject the null hypothesis. Therefore the cups’ weights are on average lower than expected. 4. We want to find out if persons of 40 years old are on average heavier than persons of 30 years old. We...
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...Hypothesis Test – Online College Mean Age Miriam M. Martin, Raymond Nearey, Maria-Lydia Lugo, Lucinda Reza PSY/315 February 23, 2013 The University of Phoenix has a significant student base and its average age, or the age mean of its enrollees, is 38 years old (Hedding, 2013). This statistic raised question as to whether it is standard that the average enrolled student in online college education is of an age greater than the usual 18-21 year old college student. This hypothesis, if the null hypothesis is rejected, would show in essence that the University as a whole is matriculated with an older student population and thus the convenience of online education for adults greater than standard college age proves to be a more convenient, attractive and efficient alternative than actually attending a college campus. Hypothesis Based upon this hypothesis question, Learning Team B arrived at the null hypothesis that if tested using a one sample T-test, Group One, or Ho, being the specific classroom’s age statistic and Group Two, or H1, representing the mean age of the University’s attendees in total, will either show that the following a) Group One—our classroom’s mean age is less than or equal to Group Two-University of Phoenix’s mean age of attendee, or; b) Group One’s mean age will be greater than the mean of age of Group Two. Stated scientifically or numerically, Ho: Group one</=Group two; H1: Group one > Group Two. Five Step Data Process To...
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...Chapter# 8 Exercises: 1.a: Matched Pairs are more appropriate. Measures are repeated over time 1.b: The matched pairs report for June 1999- March 1999 shows a difference as seen below of 0.05: [pic] [pic] 1.c: The matched pairs reports for Aug-June and Aug-March show no significant differences: [pic] 2-a The grouped means method would not be the appropriate test since statistical assumptions for the t test for groups are not satisfied with correlated data. The difference can be detected much better with a paired t-test and you may be overstating the significance if you used a group t-test rather than a paired t-test. 2-b MATCHED PAIRS: There is a small ratio of 0.1218 difference Kiln compared to regular. [pic] 3-a There is an outlier in sales for 63,438 computer sales. [pic] 3-b grouped means. 3-c: |t| 0.8874 (very small difference) [pic] 3-d Outliers: It would not be appropriate to remove the outlier because the difference in the analysis would be unfavorable due to the sale being such a drastic amount and the difference reported in question “c” being already reported as a small difference. 4-a: Yes, there is a significant difference between 2 door and 4 door impact on left legs. [pic] 4-b: The difference on compression on right legs between 2 and 4 door vehicles is not significantly different. [pic] 4-c The difference is very small on Head...
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...PAL 1 Regression * looking more in correlation * scores in how they vary on a large scale and relate with each other. * correlation and regression are non-experimental correlation * scoring high on one thing is more likely going to score on a higher thing * not manipulated * assume that e.g height and weight are correlated. * the taller you are the bigger you are * height increases weight . but cant be 100% sure * correlation has no internal validity no way on knowing your IV is causing your variable ANOVA * looks more into groups Experiment * manipulate the IV within the groups * opportunity to randomly assign to groups * it is manipulated therefore we can say that height increases weight because we change that in the first group quasi experiment * naturally assigned to groups e.g your a female and a male internal validity * isolating the IV and DV * how sure are you that IV has caused changes in your DV * causation and whats causing it to happen * e.g maybe more than one variable, and don't know if IV is causing that change * does your manipulation cause a difference in the score and how certain are you that it causes a difference in the score confounding variable * uncertain that its caused a difference * variable that interferes with the link * varies across the levels of the IV = varies systematically across the IV e.g height and age. so when...
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...Assignment 8a PSYC340 Research Methods I 1. ______ statistics are used in the process of hypothesis testing. a. Descriptive b. Null c. Alternative d. inferential 2. Inferential statistics allow us to: a. infer something about the sample based on the population. b. infer something about the population based on the sample c. infer that the sample is representative d. do all the above 3. No effect is to _____ hypothesis as effect is to _____ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 4. Ho is to Ha as ______ hypothesis is to ______ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 5. One and two-tailed hypotheses are both types of ______ hypotheses. a. null b. alternative c. directional d. non-directional 6. When using a ____ hypothesis, the researcher predicts the direction of the expected difference between the groups. a. null b. non-directional c. one-tailed d. two-tailed 7. A false alarm is to ____ as a miss is to _____. a. Type I error; Type II error b. Type II error; Type I error c. null hypothesis; alternative hypothesis d. alternative hypothesis; null hypothesis 8. Failing to reject Ho when we should have rejected it is a ____ error. a. Type I b. Type II c. null d. one-tailed 9. If researchers report that the results from their study were significant, p < .05, this...
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...41 Asia-Pacific Business Review Vol. VI, No. 2, April - June 2010 pp. 41-49, ISSN: 0973-2470 Capital Structure and Product Market Determinants: Empirical Evidence from the Indian Automobile Industry Himanshu Joshi This paper provides insights into the way in which the capital structure is determined by product market determinants, research and development activity and profitability. This paper is an attempt to test relevance of empirical evidences found in matured markets to the Indian market condition. Automobile industry is taken up for the study because of its oligopoly nature and easy availability of product prices. Some of the results are very different from the similar studies conducted in the advanced economies. It is found that the firms in the same industry can have different capital structures and there is a negative correlation between the profitability and capital structure of the companies. Interestingly, no correlation is found between R&D expenses and capital structure of the company. It was also concluded that no extra market power is attained because of high leverage. Keywords: Capital Structure, Product Market, Market Structure, Profitability, Market Power, Capital Expenditure Introduction Capital structure refers to the way a corporation finances its assets through some combination of equity, debt, or hybrid securities. A firm’s capital structure is thus, the composition or ‘structure’ of its liabilities. The modern theory of capital structure began...
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...Nonparametric Hypothesis Testing Paper Nonparametric Hypothesis Test:. Research can be focused in a number of ways, to help refine our topic to a point where we have clear hypotheses statements. If the housing industry was determined to be doing better than the rest of the economy, a hypothesis test might be in order, with mean prices greater than other housing industries. The test to determine the difference is the one sample run (Wald- Wolfowitz test to determine the mean prices to be equal to each home cost (Doane & Seward, 2007). However, March home sales were higher than expected. Our Presidents recent trip aboard has secured enough raw material and additional energy products to secure the USA zone of growth for 100 years. We believe America remains at the top of the economic world and by far the most secure in the housing industry recovery (Dohrmann B, 2011). More often than not, homeowners has maximize their time and search for the best sales for homes by searching information about homes sales and choices(Rosales L. 2011). The housing industry can also find the best buyer’s options that are available to him or her in a similar way that real estate agencies can seek the best sales on homes. However, the homes of the homeowners in the housing industry are set by the regional, state, and local expectations of the buyers and not so much by differences in qualifications of the homeowners. There are still a bright light in our housing futures across the nation (Rosales L...
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...Xochitl Jacques - Smith Hypothesis Testing/ Psychological Reasons for Depression PSY315 Statistical Reasoning Amber Lupo July 6, 2015 Psychological Reasons for Depression The number of people diagnosed with depression will be different because of either biological or psychological conditions. This hypothesis testing will include a two-tailed test with the alternative hypothesis testing as there will be a difference in either biological or psychological causes, and the null would be both cause depression equally with no difference. Our hypothesis testing will be from a survey of people with biological conditions diagnosed with depression. Our method of research is through survey studies because this will provide data that cannot be observed directly and does not allow conclusions. Biological Depression Depression is quite common and attributed as a mental disorder. Biological reasons for depression are varied leaving much research to be done to discover the true cause though there has been much headway in the knowledge of brain function. Through this research, more causes and therefore more cures have been uncovered, making it a more manageable...
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...Assignment 2 Part A QUESTION 1 Table 1: Descriptive Statistics of Age | | n | Minimum | Maximum | Mean | Std. Deviation | Age (years) | 250 | 20 | 59 | 39.16 | 10.438 | Based on Table 1, the respondents in the sample have mean age of 39.16 years with a standard deviation of 10.438 years. The 95% confidence interval on the mean age is calculated as below: Y±1.96σnN-nN-1 =39.16±1.9610.4382502000-2502000-1 =39.16±1.96 0.62 =39.16±1.22 =37.94 , 40.38 Conclusion: We are 95% confident that the mean age of the population of the respondents with smoking habits is between 37.94 years and 40.38 years. QUESTION 2 Table 2(a): Descriptive Statistics of Income for Male | | n | Minimum | Maximum | Mean | Std. Deviation | Monthly Income ($) | 120 | 1800 | 5800 | 3605.83 | 1171.962 | Table 2(b): Descriptive Statistics of Income for Female | | n | Minimum | Maximum | Mean | Std. Deviation | Monthly Income ($) | 130 | 1800 | 5800 | 3746.15 | 1244.844 | Table 2(a) shows that the mean and standard deviation of the income for male respondents are $3, 605.83 and $1, 171.96 respectively. Table 2(b), on the other hand, shows that the mean and standard deviation of income for female respondents are $3, 746.15and $1, 244.84. (i) The sample mean is calculated as below: YST= NiYiN = N1Y1+ N2Y2N = 11003605.83+9003746.152000 = 73379482000 = 3668.974 (ii) (iii) The standard error is calculated as below: SE= 1NNi2Ni-niNi-1si2ni ...
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...easier to control the size of alpha and beta if the sample size is large, therefore in determining their value they need to insure a large sampe size. Because this has to deal with drug safety and efficacy, which are important for FDA approval, the alpha should be appropriately large--meaning that you have a higher chance of rejecting the hypothesis that the drug is safe when it fact actually is. Although it shouldn't be too large since you don't want to send to waste a good product. This would give less room for type II error, which would mean you would accept the null hypothesis when if fact it is false. They don't want to say a drug is safe and effective when it actually isn't. Part B Type I error means that you reject the null hypothesis when it is true. Therefore for Set 1, you reject that the drug is safe when it actually is. And for Set 2 you reject that a drug is effective when it actually is. For each of these sets, a type I error would be of concern because you'd actually waste a good profitable product due to bad statistics. Part C Type II error means that you accept the null hypothesis when it is false. For set 1 you would accept that the drug is safe when it actually isn't. For Set 2 you would accept that a drug is effective when it actually isn't For set 1, accepting safety for a drug that could be dangerous, can lead to injury death and subsequently law suits. That's unethical and costly. A type 2...
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...regard to his points per game, assist per game, minutes played per game, blocks per game, turnovers per game, field goal percentage, three point percentage, free throw percentage, rebounds per game. These stats are used to evaluate a player, or teams overall performance. These statistics are important, because it help coaches decide who to put on the court in certain aspects of the game. A player’s efficiency rate also play a huge role when it comes to general managers, and team owners choosing free agents. Performing well on the court is what the NBA players get paid large sums of money to do, so it is important to have a way to evaluate their performance. The alternative hypothesis states that NBA players in-game production, combined to form their , has an effect on the player’s salary. The null hypothesis states that NBA players in-game production, combined to form their statistics, has no effect on the player’s salary. Figure 1 is a graph of the top 10 paid NBA players, and the contracts that they are currently signed to. #1 Kobe Bryant Total earnings: $64.5 million Salary: $30.5 million Endorsements: $34 million Bryant signed a two-year, $48.5 million contract extension in November that will keep his on-court salary tops in the NBA. #2 LeBron James Total earnings: $61.1 million Salary: $19.1 million Endorsements: $42 million The NBA's top pitchman counts Nike, McDonald's, Coca-Cola, Samsung and Dunkin' Donuts among his partners. #3 Derrick Rose Total earnings: $38.6...
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...to his points per game, assist per game, minutes played per game, blocks per game, turnovers per game, field goal percentage, three point percentage, free throw percentage, rebounds per game. These stats are used to evaluate a player, or teams overall performance.  These statistics are important, because it help coaches decide who to put on the court in certain aspects of the game. A player’s efficiency rate also play a huge role when it comes to general managers, and team owners choosing free agents. Performing well on the court is what the NBA players get paid large sums of money to do, so it is important to have a way to evaluate their performance. The alternative hypothesis states that NBA players in-game production, combined to form their , has an effect on the player’s salary. The null hypothesis states that NBA players in-game production, combined to form their statistics, has no effect on the player’s salary.  Figure 1 is a graph of the top 10 paid NBA players, and the contracts that they are currently signed to. #1 Kobe Bryant Total earnings: $64.5 million Salary: $30.5 million Endorsements: $34 million Bryant signed a two-year, $48.5 million contract extension in November that will keep his on-court salary tops in the NBA. #2 LeBron James Total earnings: $61.1 million Salary: $19.1 million Endorsements: $42 million The NBA's top pitchman counts Nike, McDonald's, Coca-Cola, Samsung and Dunkin' Donuts among his partners. #3 Derrick Rose Total earnings:...
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...Anatolia – An International Journal of Tourism and Hospitality Research 1. Introduction *Email: lee.chew-ging@nottingham.edu.my ISSN 1303-2917 print/ISSN 2156-6909 online q 2012 Taylor & Francis http://dx.doi.org/10.1080/13032917.2012.701596 http://www.tandfonline.com Anatolia – An International Journal of Tourism and Hospitality Research 349 350 C.G. Lee Anatolia – An International Journal of Tourism and Hospitality Research 351 352 C.G. Lee Table 1. The results of ADF and KPSS tests. Variable Level EX IM GDP TOU *,** and *** Statistically significant at the 10%, 5%, and 1% levels, respectively. t-Statistic and LM-statistic are reported for ADF and KPSS tests, respectively. The brackets beside t-statistic indicate the number of lagged first differences of ADF selected based on the Schwarz information criterion. The brackets beside LM-statistic indicate the choice of bandwidth parameter in the Bartlett-kernel-based sum-ofcovariances estimator selected based on Newey –West data-based automatic bandwidth parameter methods. suggest that TOU is stationary at level but non-stationary at first difference. This type of property is impossible to occur. The results of KPSS test suggest that TOU is stationary at level and first difference. Therefore, it is concluded that TOU is stationary. To investigate for a cointegrating relationship between these variables, the bounds test within the autoregressive distributed lag (ARDL) framework and...
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...Project 2: Regression Analysis Executive Summary For my regression analysis, I compared National health expenditures and Gross Domestic Products of United States by years from 1960 to 2009. The trend line indicates a positive correlation between the two variables, indicating that the two variables are related. First Regression analysis: Regression Equation: National health expenditures = - 161.1464 + 0.1677 * Gross domestic product R | 0.99297 | R Square | 0.98599 | t Statistic | 58.12474 | Standard Error | 88.03083 | p-value | 0.0000000000905707 | This shows a strong positive correlation between the two variables, based on both the large value of the t statistic and the small p-value. From my data set, there are no specific outliers which make a regression analysis weaken, therefore I will use my first data analysis as well. But before describe my data regression, I will show my second data regression which comes out after removing one outlier. For my second data regression analysis, I removed 2009 data because the GDP of United States was slightly decreased. (Again, there are no outstanding residuals for my data set, so I just demonstrate the other data regression according to the instruction) This is data regression analysis from 1960 to 2008. Second Regression analysis: R | 0.99389 | R Square | 0.98781 | t Statistic | 61.71547 | Standard Error | 77.79528 | p-value | 0.0000000000224389 | Regression Equation: National health expenditures =-...
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