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 | |...

...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...

...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...

...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...

...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...

...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...

...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...

...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....