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The relationship between IQ scores and GPA follows a null hypothesis. It can be expressed as null hypothesis: p = 0, there exists no correlation between IQ scores and GPA Alternative hypothesis: 0, quantifiable correlation between IQ scores and GPA Results of the test showed statistical significance. There exists a positive relationship between IQ score and GPA. The strength of the relationship is 0.446 with a variance of 0.2: consistent relationship of the values. A high rate of behaviors related to GPA has a significant effect on the performance of children in school. Their verbal comprehension, perceptual reasoning, working memory, and processing speed skills is clearly noted in adulthood (O’connor, 2000). We can draw conclusions from the research related to the data. It supports the impact of the subjects behaviors, and the often portray some comprehension, memory and processing speed issues. There are some notable negative correlation and some diminishing academic success. This provides predictions about the personality of a child in adulthood and adult age (Bellinger, 1989). However, a closely related variable is the EF a domain that controls the inhibitory responses that influence the behavior inhibition and inhibitory control. The study fails to prove a substantial reason to support the correlation existing between inhibitory control and fluency, planning, working memory and set shifting. This works especially in the salient to the development of the problem-solving skills required for active functioning.

Independent variables can be called trial or indicator variables. Variables can be manipulated in an examination so as to watch the impact on a dependent variable (result variable). For instance, if a teacher tells a number of pupils to sit for a math’s test, they…...

...Descriptive and Inferential Statistics Paper Casie Thibeault PSY/315 July 27, 2013 Michelle A. Williams, PhD Descriptive and Inferential Statistics Paper The very word “statistics” seems to produce anxiety in most students - anxiety produced from its connection to mathematics. The first step in controlling anxiety is to understand the connection and just how useful statistics can be for comprehending information that has been gathered. A statistic is a representation of information, and its function is to help researchers either to organize, summarize, or understand data. The ability to describe data is essential when gathering statistics. Statistics can be broken down into two basic types: descriptive statistics and inferential statistics. Descriptive statistics are a summary of information that makes the data presented more easily understood. The descriptive method is limited to only the population in which the researcher is dealing with, and only describes that particular group (Purdue OWL, 1995-2013). Inferential statistics offers a more detailed conclusion regarding the hypothesis. A benefit of the inferential method is that it can be used to take a broader view of populations, making it possible to draw conclusions about sizeable groups of people (Purdue OWL, 1995-2013). In a nutshell, the simple way to distinguish between the two would be that descriptive statistics summarize and inferential statistics draw conclusions. Both descriptive and......

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...Descriptive Statistics Analysing Age and Monthly Income 26 October 2012 Ana Rita Costa, n.º 152112179 Cátia Raquel Reais Ferreira de Araújo; n.º 152112188 INDEX 1. Introduction 3 2. Age 4 3. Monthly income 8 4. Association between variables 12 5. Conclusion 13 1. Introduction This work intends to address the request to describe two different data sets – one with observations for a discrete random variable and another with observations for a continuous random variable – according to the Business Statistics course theoretical framework. Through the use of the Descriptive Statistics’ tools – frequency tables, numerical descriptive measures and charts – we aim to examine, describe and compare distinctive aspects of a given sample. Therefore, we have chosen to analyse the age of the sample’s individuals as a discrete variable and, as a continuous variable, their monthly income. Bearing in mind the widely association between age and earnings, we decided to scrutiny such link. In order to satisfy this purpose we acceded a BES (Banco Espírito Santo) database related to savings. From there we collected information on fifty-three individual concerning age, monthly income, gender and level of education. We looked to select a broad and diversified sample likely to provide us with an accurate representation of the population. For the intended study we preceded to organize and simplify the presentation of......

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...Interpretation of SPSS output for Car Care Inc. Jacquelynn Patterson Liberty University Online Professor Lingley Busi 331-B01 October 7, 2013 1.0 Introduction Statistics in social sciences are an important aspect in making the understanding of social behaviors plausible in organizations, governments, marketers and other cohorts with same interest. Initially, statistical manipulations were conducted manually and obliged researchers to have formulas at their fingertips. This strenuous exercise was susceptible to shortcomings in case large volumes of data were to be analyzed. In addition, manual calculations depend on human nature that is vulnerable to ill health, emotional exhaustion and fatigue. As a result, there are many chances of making errors when dealing with manual calculations. This would finally affect the end results obtained. The above mentioned problems are likely to be amplified especially when dealing with a huge number of research subjects. If this is the case, it implies that marketing research data analysis would be the most vulnerable if manual statistical manipulations were embraced. This is because marketing research depends heavily on many respondents in order for the results to valid and reliable for making inferences to the whole population. Inevitably, marketers are bludgeoned into using statistical software that can handle large volumes of cases in a single command. This does not only......

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...probability report were the qualitative variables gender and quantitative variable extrinsic. The probabilities below show the probability for gender, employee’s tenure with the company, and the percentage of employees that are in different departments. Use of Statistics and Probability in the Real World We us probability in our everyday lives we are just un-ware of when it’s being used such as when watching the new and the weather man says there is a 90% chance (probability) that it will rain. When bets are made the person making the bet estimates the probabilities of a teaming winning, also when flipping a corn there is 50/50 chance (probability) of getting tails or heads on the corn toss. In school statistics are used for test scores such as the average score on test is 75% out of 100% for a group of 60 students. We can generalize that each student will mostly likely get a 75/100 on the test. The Value of Statistics Statistics play an important role in fields of people’s activities. Statistics can determine unemployment, housing, or population growth in a country (Stephanie, 2010). Statistics holds a positions in a lot of fields such as mathematics, chemistry, biology, and physics. So the application of statistics is very wide. Distributions A distribution table can be used to organize data to where it makes sense (DeGroot, 2011). Information on a distribution table can be used by AIU see the number of males and females that participated in the survey and how they......

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...Chapter 2 Descriptive Statistics: Tabular and Graphical Methods Summarizing Qualitative Data Summarizing Quantitative Data Exploratory Data Analysis Crosstabulations and Scatter Diagrams Summarizing Qualitative Data Frequency Distribution Relative Frequency Percent Frequency Distribution Bar Graph Pie Chart Frequency Distribution A frequency distribution is a tabular summary of data showing the frequency (or number) of items in each of several nonoverlapping classes. The objective is to provide insights about the data that cannot be quickly obtained by looking only at the original data. Example: Marada Inn Guests staying at Marada Inn were asked to rate the quality of their accommodations as being excellent, above average, average, below average, or poor. The ratings provided by a sample of 20 guests are shown below. Below Average Average Above Average Above Average Above Average Above Average Above Average Below Average Below Average Average Poor Poor Above Average Excellent Above Average Average Above Average Average Above Average Average Example: Marada Inn Frequency Distribution Rating Frequency Poor 2 Below Average 3 Average 5 Above Average 9 Excellent 1 Total 20 Relative Frequency Distribution The relative frequency of a class is the fraction or proportion of the total......

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...3,915 2,706 3,430 3,518 17 18 18 18 18 18 19 19 19 19 20 20 21 21 21 80 81 82 83 84 14 6 77 41 37 85 86 87 88 89 90 91 7 89 19 62 38 60 Nim's Island Indiana Jones and the 3 Kingdom of the Crystal Skull 82 The Reader Journey to 26 the Center of the Earth 2 Iron Man Dr. Seuss' 10 Horton Hears a Who! 12 Gran Torino Slumdog 16 Millionaire 5 WALL-E 100,137,835 317,101,119 4,264 21 92 93 168,051 21,018,141 98,618,668 45,012,998 271,720 360,018 63,087,526 34,194,407 101,704,370 318,412,101 154,529,439 148,095,302 141,319,928 223,808,164 1,203 2,830 4,154 3,961 3,045 2,943 3,992 22 22 22 25 27 28 28 94 95 96 97 98 99 100 1 The Dark Knight 158,411,483 533,345,358 4,366 33 DESCRIPTIVE STATISTICS Opening Gross Mean Standard Error Mode #N/A 26,160,050 2,334,918 #N/A Total Gross 85,228,635 7,405,866 Theaters 2,958 73 2798 Standard Deviation Range Minimum 23,349,183 158,287,970 123,513 74,058,662 504,700,545 28,644,813 735 3,679 687 Q1 Median 13,314,517 19,066,030 38,020,299 61,833,338 2,627 3,029 Q3 Maximum 61,457,841 158,411,483 102,098,073 533,345,358 3,470 4,366 The data tells me that the success of movies varies wildly. For example, consider the the "total gross" variable The movie that brought in the least was about half of the median, where the movie that brought in the most was While most movies bring in a modest amount, a few blockbusters do really well. The mean total......

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...Descriptive Statistics kWh 1st Hour Operational Day This data set has a p-value of much less than .05 so it is classified as skewed, or not normally distributed. In this case the Median and the IQR are better indicators of the distribution. Central Tendency: Mean = 113.76; Median = 108; Mode = 128 Dispersion: Standard Deviation = 39.05; IQR = 60; Range = 169 Number: n = 147 Min/Max: 53/222 Overnight Low OAT in F° This data set is distributed normally. Central Tendency: Mean = 66.3; Median = 65.3; Mode = 67.8 Dispersion: Standard Deviation = 7.9; IQR = 11.7; Range = 36.9 Number: n = 147 Min/Max: 48.9/85.8 Confidence Interval: Lower = 65.04; Upper = 67.62 OAT Strata OAT < 70; OAT ≥ 70 Descriptive Statistics Interpretation kWh 1st Hour Operational Day The 60 minutes from 7:00a – 7:59a combine to make the first hour of the operational day for this school. The number of kWh consumed as measured by the electric meter and collected by the website, MYPVDATA.com, are tabulated for each day in the time period, August 1 through October 31. This data is taken from each of these time periods over three years, 2012 through 2014. This gives us a sample of 147 individual electric meter readings for the electricity consumed in the 7:00a hour on operational days. The kWh is a measurement of electrical demand. Things that consume electricity are for instance, air conditioners and heaters, lights and other peripheral equipment. The electricity......

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...Dave’s Crash Course in Statistics using SPSS 1.0 Classifying the different types of data There are four types of variables: nominal, ordinal, interval and ratio. Distinguishing between these types of variables is important, as several statistical tools may only be used for certain types of data. Nominal variables: where values are assigned to categories in no particular order. This assignment of values is arbitrary and holds no particular meaning or order to them. For example, “sex” where 1=male 2=female “marital status” where 1=never married 2= married 3=defacto “yes/no type questions” where 1=yes 2=no. Ordinal variables: where values are assigned to categories that are related to each other in some logical order – such as ascending or descending order. For example, “age group” 1=under 21yrs 2=21-35yrs 3=35-49yrs 4=50 yrs and over “education” where 1=high school completed 2=tertiary studies completed 3=post-graduate studies completed. The higher the value assigned, the higher the category (ie. higher age group or education level). Interval variables: where the values assigned are ordered in the same way as ordinal variables, however, the intervals or distances between the categories are equally spaced. For example, “please rate the importance of the following attributes…” according to the scale 1----------2----------3----------4----------5 where......

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...Descriptive Statistics Pain Study “The kappa opioid nalbuphine produces gender-and dose-dependent analgesia and antianalgesia in patients with postoperative pain” was a study that was performed to observe gender-specific patient response to varied doses of nalbuphine, an opioid pain medication, following oral surgery (Gear, Miaskowski, Gordon, Paul, Heller, & Levine, 1999). In this study, the researchers asked participants to rate their pain on a 10 cm visual analog scale (VAS) just before drug administration to obtain a baseline measurement, and again at 20 minute intervals thereafter (Gear et al., 1999). The demographic characteristics and descriptive statistics of the 131 participants are provided in Table 1 of the study (Gear et al., 1999). To aid in interpretation of the data collected in the research experiment, the researchers provide the reader with information using both ratio and ordinal data measurements. The weight of the participants is given as a mean, or average, and is considered ratio measurement. This is important data because weight is a variable that is considered when calculating dosage requirements. For each dose of pain medication given, as well as the placebo, the weight in kilograms for both men and women is averaged in the table. The data appropriate and meaningful since the average weight of participants in each dose category is similar save for the weight differences between men and women. Ratio measurement is considered the......

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...weight. (b) What sample size would be necessary to estimate the true weight with an error of } 0.03 grams with 90 percent confidence? (c) Discuss the factors which might cause variation in the weight of Tootsie Rolls during manufacture. (Data are from a project by MBA student Henry Scussel.) Tootsie Answers: a) Confidence intervals are used to find a region in which we are 100 * ( 1 - α )% confident the true value of the parameter is in the interval. In order for the Confidence Interval to be valid you must have data from a normal distribution, at least if you are using the method here. If you do not have normal data then this type of confidence interval is not valid. To clear up the notation I will use here. "t" is the test statistic and "t_(n-1)" is a Student t random variable with n - 1 degrees of freedom, e.g. a Student t random variable with 18 degrees of freedom is denoted as t_18.For small sample confidence intervals about the mean you have: xBar ± t * sx / sqrt(n) where xBar is the sample mean t is the t - score with n - 1 degrees of freedom such that α% of the data in the tails, i.e., P( |t_(n-1)| > t) = α sx is the sample standard deviation n is the sample size The sample mean xbar = 3.3048 The sample standard deviation sx = 0.1319889 The sample size n = 10 The t score for a 0.9 confidence interval is the t score such that 0.05 is in each tail. t = 1.833113 The confidence interval is: ( xbar - t * sx / sqrt( n ) , xbar + t *......

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...Chapter 2: Descriptive Statistics Prerequisite: Chapter 1 2.1 Review of Univariate Statistics The central tendency of a more or less symmetric distribution of a set of interval, or higher, scaled scores, is often summarized by the arithmetic mean, which is defined as [pic]. (2.1) We can use the mean to create a deviation score, [pic] (2.2) so named because it quantifies the deviation of the score from the mean. Deviation is often measured by squaring, since it equates negative and positive deviations. The sum of squared deviations, usually just called the sum of squares, is given by [pic] (2.3) Another method of calculating the sum of squares was frequently used during the era that preceded computers when students would work with calculating machines, [pic] (2.4) Regardless whether one uses Equation (2.3) or Equation (2.4), the amount of deviation that exists around the mean in a set of scores can be averaged using the standard deviation, or its square, the variance. The variance is just [pic] with s being the positive square root of s2. We can take the deviation scores and standardize them, creating, well; standardized scores: [pic]. (2.5) Next, we define a very important concept, that of the covariance of two variables, in this case x and y. The covariance between x and y may be written Cov(x, y). We have [pic] [pic] (2.6) where the[pic]are the deviation scores for the x variable, and the [pic]are......

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...modal class being again 20- which means that the greatest proportion of my sample (50%) got grades between 20 and 25. The mode of this distribution is approximately 23. (See table 3 in the Appendix). Numeric Descriptive measures for the sample: * The mean for the sample is 21.07 which is very close to that of the population meaning that the sum of deviations of the grades from that grade is zero. 21.07 is the average grade of those students which can be considered to be good but not excellent. * The median for the sample is 22.5 indicating that 50% of the students in my sample (15 students) got grades less than 22.5 and the other 50% got more than 22.5. * The first quartile of this distribution is 18.75 meaning that 25% of the students got grades less than 18.75, while my third quartile is 24.50 indicating that 75% of the students got grades less than 24.50. * The Interquartile Range is 5.75 which is smaller than that of the population indicating that the variability of the middle 50% of my sample is less than that of the population. * By comparing the coefficient of variation of the population and sample we find that the variability of the sample is greater. (See tables 1 and 3 in the Appendix). Conclusion: From the graphs and descriptive measures obtained from both the sample and population, we can conclude that these students are of middle level but not a low middle one. Most of them have passed but we can find differences between them......

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...Marketing Research Fall 2011 Exercise: SPSS 5. Hypothesis test The MBA programme leader is interested to know if there is any significant average age difference between males and females and if there is which is the older group. a. Suggest a null hypothesis and an alternative hypothesis for testing the mean age for male and female students. μ0: The average ages of males and females are the same. μ1: The average ages of males and females are not the same. b. Carry out an appropriate test to compare the mean age for the two sexes, and interpret your results. Since the goal is to compare two means and that the data is of ratio scale, One-Way ANOVA is the appropriate test. Here we have gender as the factor and age as the dependent variable, and we choose the common 0.05 level of significance. Figure 5.1 is the resulting ANOVA table. | | | | | | |3.131a |2 |.209 | | |3.433 |2 |.180 | | |.543 |1 |.461 | | |40 | | | Figure 6.1 Cross table of satisfaction and sex at α=0.05 The p-value, which is 0.209, is very obviously greater than our chosen level of significance, 0.05. The null hypothesis...

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...Report Business Research Methods The third homework (about the descriptive statistics) Question 1: Explain the difference of Mobile Contents Use by gender Crosstabs Case Processing Summary | | Cases | | Valid | Missing | Total | | N | Percent | N | Percent | N | Percent | Sex * Music | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Movie | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * DMB | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Phone Decorating | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Sport | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Game | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * MMS | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Adult | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Animation | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Mobile banking | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Map | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Stock Trading | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Chatting | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * News/Weather | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Music Crosstab | | Music | Total | | 0 | 1 | | Sex | 1 | Count | 91 | 97 | 188 | | | % within Sex | 48.4% | 51.6% | 100.0% | | | % within Music | 65.0% | 60.6% | 62.7% | | 2 | Count | 49 | 63 | 112 | | | % within Sex | 43.8% | 56.3% | 100.0% | | | % within Music | 35.0% | 39.4% | 37.3% | Total | Count | 140 | 160 | 300 | | % within Sex | 46...

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...Descriptive Statistics University of Phoenix July 18, 2011 RES/341 Descriptive Statistics In the previous weeks we researched the issues that have been affecting the real estate business; such as home prices in which are making it difficult for homes to sale. The current issues that are affecting the housing market in today’s society have to do with homes being over priced due to size, location, and square footage. In the past weeks Learning Team C had to research the problems in the housing market to determine the reasons why homes that are similar in size tend to be more expensive than others and the factors that go into these prices. The research problem that Learning Team C encountered has to do with looking at: 1. Price, 2. Bedrooms, 3. Size, 4. Pool, 5. Distance, 6. Township, 7. Garage, and 8. Baths, to determine why some of the listed homes are more expensive than others homes in the area. In week three we turned the research over to researching peer-reviewed articles to get an even clearer picture of the whole aspect of real estate, which included; faulty work from appraisers, estimating value of homes, and how size play a major role in the price of a home. Moving on into week four, Learning Team C will evaluate all the data from the previous weeks and draw a conclusion based on all current research finding of why similar homes tend to be more expensive than others. Frequency Distribution The following graphics and data analysis will identify the......

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