Statistics Coursework Plan –
In this project, I will be investigating how accurately students can estimate an angle size and the length of a line. I am investigating it to see if age, gender and mathematical capabilities have an effect on how accurate students can estimate a length of a line and an angle size. I will be using secondary raw data which is given to me to my teacher who has collected the data from other students. The accuracy of the data is unknown and also human errors are also likely Outliers and anomalies distort the mean of the data taking it to either of the two extremes. To avoid any Outliers or anomalies affecting the accuracy of this study, I will remove them before taking the sample size of around 80-100 students and I will be using stratified sampling so each category categorized by gender, age and maths set have a equal proportion in the sample as in the total population so the results are as accurate as possible. Any outliers which I may have missed can be eliminated by using the formula – Q1-(1.5)*(IQR) or Q3+(1.5)*(IQR). The three hypotheses I will be investigating will be:

Boys estimate the lengths of a line and angle sizes better than girls. – I will be investigating this as boys tend to partake in activities which involve measuring more than girls and so are better than girls at estimating lengths of a line and angle sizes. Year 8 students estimate the angle sizes and lengths of a line better than Year 10 students. – I will be investigating this because Year 8’s may not have the pressure of other subjects yet as they do not have any real exams however Year 10 students may have been preoccupied with other thoughts and so are less accurate at estimating the lengths of a line and angle sizes. Students who are better at estimating the lengths of a line are also good at estimating the angle sizes. – I will be investigating this as students who are good at estimating one are likely to be better at estimating the other as they have...

...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...

...1. Introduction
This report is about the case study of PAR, INC. From the following book: Statistics for Business an Economics, 8th edition by D.R. Anderson, D.J. Sweeney and Th.A. Williams, publisher: Dave Shaut. The case is described at page 416, chapter 10.
2. Problem statement
Par, Inc. has produced a new type of golf ball. The company wants to know if this new type of golf ball is comparable to the old ones. Therefore they did a test, which consists out of 40 trials with the current and 40 trials with the new golf balls. The testing was performed with a mechanical fitting machine so that any difference between the mean distances for the two models could be attributed to a difference in the design. The outcomes are given in the table of appendix 1.
3. Hypothesis testing
The first thing to do is to formulate and present the rationale for a hypothesis test that Par, Inc. could use to compare the driving distance of the current and new golf balls. By formulation of these hypothesis there is assumed that the new and current golf balls show no significant difference to each other. The hypothesis and alternative hypothesis are formulated as follow:
Question 1
H0 : µ1 - µ2 = 0 (they are the same)
Ha : µ1 - µ2 ≠ 0 (the are not the same)
4. P-value
Secondly; analyze the data to provide the hypothesis testing conclusion. The p-value for the test is:
Question 2
Note: the statistical data is provide in § 5.
-one machine
-two...

...typically have? You take a random sample of 51 reduced-fat cookies and test them in a lab, finding a mean fat content of 4.2 grams. You calculate a 95% confidence interval and find that the margin of error is ±0.8 grams. A) You are 95% confident that the mean fat in reduced fat cookies is between 3.4 and 5 grams of fat. B) We are 95% confident that the mean fat in all cookies is between 3.4 and 5 grams. C) We are 95% sure that the average amount of fat in the cookies in this study was between 3.4 and 5 grams. D) 95% of reduced fat cookies have between 3.4 and 5 grams of fat. E) 95% of the cookies in the sample had between 3.4 and 5 grams of fat. Determine the margin of error in estimating the population parameter. 12) How tall is your average statistics classmate? To determine this, you measure the height of a random sample of 15 of your 100 fellow students, finding a 95% confidence interval for the mean height of 67.25 to 69.75 inches. A) 1.5 inches B) 0.25 inches C) 1.06 inches D) 1.25 inches E) Not enough information is given. 12) 11) 10)
3
Construct the indicated confidence interval for the difference between the two population means. Assume that the assumptions and conditions for inference have been met. 13) The table below gives information concerning the gasoline mileage for random samples of trucks of two different types. Find a 95% confidence interval for the difference in the means m X - m Y. Brand X Brand Y 50 50 20.1 24.3 2.3 1.8 13)...

...that we are accepting the alternative hypothesis and this statement works vice versa. In this case that means that the null hypothesis can be rejected or disproving. For the data set that was given the null hypothesis also known as H-nought was µ1=µ2, while the alternative hypothesis is µ1<µ2. Null hypothesis states that the amount of rural nurse homes was equal to the average amount of beds used. Alternative hypothesis states that rural area nursing homes uses fewer amounts of beds. The claim indicated to what kind of test was going to be used and since I claimed that the rural area were going to have a lower average number of beds it states that the shaded area on the critical value test will be less than zero.
Table 1. Descriptive statistics for the given null and alternative hypothesis that includes the sample, mean, median, standard deviation, maximum values, and minimum values.
Sample Size
Mean
Median
Standard Deviation
Maximum Value
Minimum Value
Rural Area
34
0.6538
1.0000
0.4803845
1
0
Bed
4850
93.27
88.00
40.85273
244.00
25.00
Figure 1. This figure illustrates the critical value test for the left-tailed test. The critical value that was needed for the test was -1.692 according to the t-table since our sample size was 34. Used the degree of freedom formula to find the critical value.
Figure 2. This figure reflects to the p-value. When we figured out the p-value we used pt(t,33). Since pt(t,33) equaled 0.0137855 that indicated...

...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...

...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...

...BUS 105e:
Statistics
By Dr Tony Halim
GBA: 27 February 2013
Done by:
Koh En Song Andrew (Q1211397)
Melissa Teo Kah Leng (E1011088)
Woon Wei Jie Jared
T 04
1.
Over the span of 100 days, the total revenue for Unicafe North and Unicafe West is $21876.60 and $22042.00 respectively. The average revenue for Unicafe North is $218.77. The average revenue for Unicafe West is $220.42. The highest revenue occurred on the 88th day for both outlets. The lowest revenue occurred on 39th day for both outlets. Generally, both outlets earn roughly the same amount of revenue each day.
2a.
Confidence interval is a range of values constructed from sample data so that the population parameter is likely to occur within that range at a specific probability (Lind, Marchal & Wathen, 2013).
Using the 95% level of confidence, the confidence interval for Unicafe West is 220.42 6.211. The confidence interval limits are $214.21 and $226.63 (rounded off to 2 decimal places).
Using the 95% level of confidence, the confidence interval for Unicafe North is 218.766 5.571. The confidence interval limits are $213.20 and $224.34 (rounded off to 2 decimal places).
In the event that Mr Yeung wants to predict his potential revenue for the next one hundred days, 95% of the confidence intervals would be expected to contain the population mean. The remaining 5% of the confidence intervals would not contain the population mean, average revenue earned per day....

...Statistics for Business Intelligence – Hypothesis Testing
Index:
1. What is Hypothesis testing in Business Intelligence terms?
2. Define - “Statistical Hypothesis Testing” – “Inferences in Business” – and “Predictive Analysis”
3. Importance of Hypothesis Testing in Business with Examples
4. Statistical Methods to perform Hypothesis Testing in Business Intelligence
5. Identify Statistical variables required to compute Hypothesis testing.
a. Correlate computing those variables from the data available in normalized tables arranged in row x columns.
6. Computing Statistical Hypothesis Testing for Business Decisions using Algorithms
7. User Interface Development for Presentation of Hypothesis feature
8. How does it fit in Prajna?
1. What is Hypothesis testing in Business Intelligence?
Hypothesis Testing – is used to prove or disprove the research (Business proposed decision) hypothesis by providing more measurable or concrete hypothesis statement. for example, a research hypothesis could be that the stock market index reflects the state of monsoon in the country. A statistical hypothesis might look at the values of the index with the percentage increase or decrease in rainfall during the year compared to previous years.
Hypothesis Testing is a study about
* How to test a sample against a benchmark?
* How to assess the risk of incorrect decisions?
Identifying the confidence intervals for a...