Dani Socher
Statistics Project
Mr. Seufer
1/15/2013
Shooting Guards and Field Goal Percentage
In the NBA, games are won by shooting the ball well, which makes sense. The more times your team’s players get the ball in the hoop, the more points are scored. And the more points scored, the more games won. Obviously, it is a little more complicated than that in reality, given how important defense is. But field goal percentage should be a solid indicator of games won. Field goal percentage is as simple as made shots divided by shot attempts. Once we discover if field goal percentage indicates team success, we should then examine how teams can choose players in the draft with higher field goal percentages; better shooters. We will be focusing on shooting guards, as they are the players who shoot unassisted the most. To start, a scatterplot searching for the correlation between winning percentage of NBA teams so far in 2012-2013, and the NBA team field goal percentages. Win%

Win%
FG%
FG%

There is a fairly strong positive correlation, as can be seen from the scatterplot. The correlation coefficient, or R, should be examined to confirm what the eye test tells us. R = .638
.638 shows a fairly strong positive correlation, as it is close to 1. It is clear that a higher field goal percentage means your team has a much better shot at finishing with a good winning percentage, although it is not a perfect rule. With this knowledge, one would assume that teams should attempt to draft players who will shoot high field goal percentages. Obviously, teams do attempt to do this. Are there certain physical characteristics that predict field goal percentage, and thus NBA success? For the sake of this study, the focus will be on shooting guards. The players at the shooting guard position shoot more in isolation plays than players at any other position in the NBA. Shots out of isolation are unassisted, which means that their chances at going through the hoop are almost...

...Yarn Production
Statistics - Final Project
Table of contents
1.0 Introduction 3
1.1 The aim of the study 3
2.0 Methodology 3
2.1 Correlation 4
2.2 Independent Samples Test 4
2.3 Two Way Anova 4
2.4 One Sample T-Test 4
2.5 Regression 5
2.6 Histogram 5
3.0 Data Analysis 5
3.1 Question 2 5
3.2 Question 3 6
3.3 Question 4 7
3.4 Question 5 8
3.5 Question 6 8
3.6 Question 7 10
4.0 Conclusions 11
5.0 References 12
1.0 Introduction
A company with factories located in New York and Michigan produce yarn, using two different types of machines; JC980 and VH80. Each factory uses only one machine type. The sample size is at 1 391 factories.
The data are collected from different days and from different factories from both cities. The variables are as follows.
• City: The city where the factory is located (New York = 1, Michigan = 0).
• Machine: Type of the machine used in the factory to produce yarn (JC980 = 0, VH80 = 1).
• Worker: Number of workers in the factory.
• Product: Amount of the yarn production of the factory (Kg).
• Resource: Amount of consumed resources to produce the yarn (Kg).
• Detective: Amount of detective (damaged) production (Kg).
• Cost: General cost (USD $).
• Sale: Sales amount (USD $).
1.1 The aim of the study
The purpose of the study is to analyze the data. I.e. draw conclusions about the variables and find out how to deal with the problems.
One of the...

...Time | Contributor | Contribution |
Ancient Greece | Philosophers | Ideas - no quantitative analyses |
17th Century | Graunt,Petty
Pascal, Bernoulli | studied affairs of state, vital statistics of populations studied probability through games of chance, gambling |
18th Century | Laplace, Gauss | normal curve, regression through study of astronomy |
19th Century | Quetelet
Galton | astronomer who first applied statistical analyses to human biologystudied genetic variation in humans(used regression and correlation) |
20th Century (early) | PearsonGossett (Student)
Fisher | studied natural selection using correlation, formed first academic department of statistics, Biometrika journal, helped develop the Chi Square analysisstudied process of brewing, alerted the statistics community about problems with small sample sizes, developed Student's testevolutionary biologists - developed ANOVA, stressed the importance of experimental design |
20th Century (later) | Wilcoxon
Kruskal, Wallis
Spearman
Kendall
Tukey
Dunnett
Keuls
Computer Technology | biochemist studied pesticides, non-parametric equivalent of two-samples testeconomists who developed the non-parametric equivalent of the ANOVApsychologist who developed a non-parametric equivalent of the correlation coefficientstatistician who developed another non-parametric equivalent the correlation coefficientstatistician who developed multiple comparisons...

...
Final Project: Nyke Shoe Company
Barbara Greczyn
STA 201 - Principles of Statistics
Instructor Alok Dihtal
April 26, 2015
Introduction
Nyke Shoe Company has been in business for over 50 years. Over the last five years, the company has been undergoing some financial hardship due to an erratic market and an inability to understand what the consumer actually needs. In a last ditch effort to avoid bankruptcy, they have adopted a new business model which entails the development of only one shoe size. In order to achieve this goal, statistical data must be utilized and applied to make the best choice. The data used will be explained to the fullest and a conclusion will be then obtained.
Methodology
A sample group of 35 participants was gathered, 18 females and 17 males. Their heights and shoe sizes were gathered and their data was processed in three categories: shoe size, height, gender. Descriptive statistics was applied to three separate data sets, one with all participants included, one sets with just female participants, and one with just male participants. Then a two sample t-test was conducted with the assumption that there were unequal variances amongst both male and female data sets.
Results
There is a normal distribution of the data with ranges in size from size 5 to size 14 amongst the participants. With these ranges, the mean is 9.142, with a standard deviation of 2.583 and a variance...

...PEARSON’S PRODUCT-MOMENT CORRELATION COEFFICIENT
ANSWERS TO EXERCISE 23
Question 1
The r value for the relationship between Hamstring strength index 60o and the Shuttle run test is -0.149. This r value shows a weak correlation between the two variables, as it is less than the 0.3 threshold for significance. Therefore, the r value is not significant.
Question 2
Between r=1.00 and r=-1.00, there is no difference in terms of strength. Both values are on the extreme ends of the spectrum and signify the maximum significance within the r value scale. A value of 1.00, whether negative or positive, shows that the two variables have a perfect linear relationship, and as such, the independent variable can be used to accurately predict the value of the dependent variable. The only difference is that the negative value signifies that a rise in one variable causes the corresponding variable to drop while the positive value signifies that the rise in one variable causes the corresponding variable to increase in value as well. But strength wise, they are similar.
Question 3
The relationship between the hamstring strength index 60o and shuttle run test index is a negative one. This is signified by the negative nature of the r value (-0.498). A negative relationship occurs when a rise in one variable causes the corresponding variable to decrease in value.
Question 4
This research study had the primary objective of measuring the relationship between muscle strength and functional...

...Diversified Global holdings group
Strategic analysis
business analytics department for CCResorts Central coast
Strategic analysis
BUsiness analytics Department
Executive summary
This study was produced on behalf of the Business Analytics Department at DGHG for CCResorts in order to examine market research and determine how the venture is progressing. The company provided a data sample from the past 12 months with 200 entries, each with 6 variables. The aim of this report is to evaluate the success of CCResorts in fulfilling their key performance indicators as outlined in their business plan, determines the clientele that are attracted to CCResorts and analyses the effect of different variables on the expected expenditure of the customers. The statistical analysis yielded several significant conclusions discussed in terms of their implications for CCResorts. The sample meets with key performance indicator 1 with over 40% of guests staying the full week. There is sufficient evidence to suggest that over 40% of the total population also stay 7 days at CCResorts. On average, majority of customers do not spend more than $255 per day at the resort. Despite this, there are certain demographics that are more likely to achieve a higher expenditure per day. Firstly, the age of the guest impacted their daily expenditure with customers who were older tending to spend slightly more than their younger counterparts. Furthermore, guests who stayed in large groups had a greater...

... |
D. The river/lake where a fish was captured
4. Managers study the number of days per month over the last year that employees in the payroll department called in sick to determine the averages they can expect next year. Collecting the data and determining the averages last year is an example of what type of statistics? Determining the averages they can expect next year is an example of what type of statistics?
A. They are both examples of inferential statistics because averages are inferred in both instances.
B. Inferential statistics; descriptive statistics.
C. They are both examples of descriptive statistics because they deal with analyzing data.
D. Descriptive statistics; inferential statistics.
5. The grades that a random sampling of students in the psychology degree program received over the last decade of "Abnormal Psychology" classes are an example of what statistical concept?
A. The grades are an example of a parameter.
B. The grades are an example of a sample.
C. The grades are an example of a population.
D. The grades are an example of a statistic.
6. What method is used to sample a population so that it is representative of the population?
A. All but the samples that appear to have the lowest and highest values are selected.
B. Samples are chosen at random from the...

...Syllabus for Statistics
Course No. 21090024
Period：54
Credit：3
Course Nature：Compulsive
Assessment: Usually 10%, Group Work 20%, Final Exam70%
Textbook：
Statistics(3rd Edition)，
Junping Jia，Xiaoqun He，Yongjin Jin，China Renmin University Press，2007
Reference：
Statistics for Business and Economics(7th Edition)
Anderson, D.R., & Sweeney, D.J. & Williams, T.A.
1.Introduction
Statistics is a core curriculum for students in finance and economics major, which is a science method that starts with data to study the status and development of the society economic phenomenon. This course mainly tells us the skill how to collect and collate information and the methods how to do with quantitative analysis and comprehensive evaluation. Specific content include: statistical design, statistical research, aggregate indexes, relative indexes, average indexes, sign variability indexes, time series prediction, statistics indexes, sample inferred, correlation analysis, aggregate indexes for the national economy etc.
2. Proportion of Course Hours
Chapter
Proportion
Chapter1 General Introduction and Statistical Data
2
Chapter2 Introduction of Descriptive Statistical Data: Tabular and Graphical Methods
2
Chapter3 Statistical Data summarize and Related cases
2
Chapter4 Introduction of Statistical Sampling and Sampling Distribution
6
Chapter5 Parameter Estimation
4
Mid-term Questions and Discussion
2
Chapter6 Hypothesis...

...Statistics Cheat Sheet
Proportion = Frequency x 100 = Percentage Total No | Z score (standardised value)-how many sds from the mean the value liesZ score = data value – mean Standard deviation | Metric Data = ExploreCategory = Frequencies |
Bigger sample size will give a narrower confidence interval range (more specific) outliers affect the mean but not the median – this is why the median is preferred here.mean | | Reports -Only give confidence interval if significant-All values to 2 dec pts except the p-value Experimental = IV is manipulated to see the effect on the DV
Observational = Information just observed & recorded |
P-Value Significant Figurep-value < 0.05 = Significantp-value < 0.05 = Not SignificantReport p value 0.000 as <0.001
The probability that our test statistic takes the observed value Always leave at 3 decimal places | Levene’s Test-Used to test if equal variancesIf significant (<0.05)– use equal variances not assumed rowIf not significant (>0.05)– use equal variances assumed rowReport confidence interval as the 95% confidence interval indicates... | Dependent Variable = the variable in which we expect to see a changeIndependent Variable = The variable which we expect to have an effect on the dependent variable Example: There will be a statistically significant difference in graduation rates of at-risk high-school seniors who participate in an...