Research Question:
Which of the determinants Sex, Class or Age affect the survival of passengers?

Introduction:

According to the research question, it can be defined the determinants affect the survival of passengers.

Methods:

The 458 of passengers are going to be divided into died and survived. It is compared by the Two-sample t-test. Those graphs have been defined in three different determinants, which are sex, class and age. The first graph shows that female have more frequency to survive compare to male. The male has a frequency to die more than 5 times than the female. It is obvious that the female is the most survivor in between the both sexes. The second graph shows the relationship between the class and the frequency of the survivor. Between the 1st, 2nd and 3rd class, the 1st class of passengers are more frequency to survive than the 2nd and 3rd class of passengers. On the other hand, the 3rd class of passengers has higher chance to die, which is 3 times the 1st class of passengers. In the box-plot graph, died age is similar than survived age. The main difference of the two-sample t-test is that the survived box-plot has larger outliers that are the larger age points.

Result:

Actually, it can be obviously seen that the female has more frequency to be survived than male. Also, the 1st class of passengers are less frequency to be died but 3rd class of passengers are more frequency to be died. Quoting some two-test statistic, the degree freedom is 456. The t is 1.047 and the p-value is 0.2957 which >0.05 and does not reject Ho.

Conclusion:

In conclusion, the determinant of Sex can affect the survival of passengers as it can be considered in the first graph. The female survivor rate is twice more than the male.

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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 of 6.670....

...Conduct a binomial tests using the RELIGIOUS variable (labelled QA8 Facility is affiliated with a religious organization) to test this claim. Provide the relevant output and write the report for your test in the style presented in the course materials.
Previous research has suggested that more than 5% of institutions are affiliated to a religious organisation.
In a sample of 2000 institutions, 7.3% were affiliated with a religious organisation. This sample proportion is more than the test proportion of 5% which is consistent with our hypothesis that more than 5% of institution are affiliated to a religious organisation. A Binominal test shows that the difference is significant, n = 2000, p < 0.001. The 95% confidence interval indicates that between 6% and 8% of all institutions are affiliated to a religious organisation.
There is sufficient evidence to conclude that more than 5% of all institutions are affiliated to a religious organisation
4 – Previous research has suggested that the average number of female outpatients in these institutions is about 200. Use a one-sample t-test on the OPSEXTOTF variable (labelled QB5b_num_fem. Female outpatients – Total) to test whether the average number of female outpatients is different from 200. Provide the relevant output and write the report for your test in the style presented in the course materials.
Previous research has suggested that the average number of female...

...
Statistics
BUS308: Statistics for Mangers
Instructor:
Learning Statistics
Statistical data has become an item that we see all around us in our everyday lives, from television programs talking about selling products or politicians using data to show how they perform in their jobs, in hopes to be reelected. Throughout the course in Statistics for Managers, I have learned many things on how the use of statistical information can help me to understand these items and also to help me to perform my job and understand the day-to-day operation of the company. With the use of statistics, anyone can find out information and details on most anything, allowing them to understand a business better or to make better decisions in their everyday life. Because statistics is all around us, using and understanding this information is important to find answers to questions, to make better decisions, and understand how things work.
Some of the types of information I have learned to use is through the use of descriptive and inferential statistics. According to the textbook for statistics, “descriptive characteristics can provide a great economy when data sets are large. Inferential statistics are utilized when the sample’s characteristics are important for what they reveal about the entire population”. (Tanner & Youssef-Morgan, 2013) Even though there are...

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BUS 308 STATISTICS FOR AMANAGERS
BUS 308 Week 1 DQ 1 Language
Numbers and measurements are the language of business.. Organizations look at results, expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of ? How are the measures collected, and how are they summarized/described? How are they used in making decisions? (Note: If you do not have a job where measures are available to you, ask someone you know for some examples or conduct outside research on an interest of yours.)
BUS 308 Week 1 DQ 2 Levels
Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data). For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track? How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data? Does looking at the variation in the data give us a different understanding of results?
BUS 308 Week 1 Problem Set Week One
Problem Set Week One. All statistical calculations will use the Employee Salary Data set (in Appendix section).
Using the Excel Analysis ToolPak function Descriptive Statistics, generate descriptive statistics for the salary data. Which variables...

...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....

...BIO 2003 SUMMATIVE ASSIGNMENT 2
Introduction:
The report analyses the result of a study on workers from brick and tile industries conducted by the Health and Safety Laboratory (HSL). HSL put down few criteria’s to the workers which being that neither of the workers from the tiles and brick industries should have worked in both the industries and that they did not smoke. The criteria’s put across was an assurance to attain reliable results.
The essence of the study lies in detecting any difference in the health of the workers in these industries (as identified by cell damage) if any and also to determine if any relationship exists between the length of service and the recorded health effect.
The Null Hypothesis (Ho) states that no difference in the median between the percentage-damaged cells of the workers from the brick and tile industries is observed. Null Hypothesis for the correlation study also states that there is no correlation between the health effects of the workers and the time period they have worked in the industries.
Nonetheless the Alternative Hypothesis (H1) states that the median percentage of damaged cell of the workers in the brick industry is different when compared to the median percentage of damaged cells of workers of both the operations. H1 for the correlation study states that correlation exists between the time period the workers have worked in the industry and their health effects.
Analysis will be carried out with the help of...

...3ER PARCIAL
Inferential statistics
Sampling
* The purpose of sampling is to select a set of elements (sample) from a population that we can use to estimate parameters about the population
* The bigger the sampling, the more accurate our parameters will be.
example:
In the experiment of deciding if CEGL girls are smarter that CEGL boys, which would be your statistical hypothesis?
Hypothesis testing
But now, you already gathered information about a sample
No, you will test if your hypothesis are true or not
Hypothesis testing involves testing the difference between a hypothesized value of a population parameter and the estimate of that parameter, calculated from the sample
example:
If you want to know if CEGL girls are smarter that CEGL boys, you ask a few girls/boys their grades and compare averages, we will use Excel to compare the population and sample means. If the difference is too high, we can’t compare.
In statistics, the hypothesis to be tested is called “null hypothesis” and has the symbol “Ho”
The other option of the hypothesis is the “alternative hypothesis” and its symbol is “Ha”
1 Ho: “There is no difference between (independent variable) and (dependent variable)”
2 Ha: “There is a difference between (independent variable) and (dependent variable)”
example:
In the experiment of deciding if CEGL girls are smarter that CEGL boys, which would be your statistical hypothesis?
Ho: There is no...

...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...