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– Using your individually selected topic and research question, LOCATE a “population” that would be appropriate to use for your ‘theoretical’ study. Find a suitable source of demographic data (age, gender, education, occupation, income, access to medical care (insurance status), immunization status, etc.) and compare your class ‘survey’ sample demographic statistics to your chosen population. Be prepared to explain the logic behind any restrictions or bias in your chosen data source. – You may find the dataset useful of Titanic document (attached below) to practice the measures of central tendency on. My topic is about managing floods in Saudi Arabia (leteriture review about the topic attached below) you must read it to have a complete idea about it. The research question is: What preventive methods should Saudi use to avoid the occurrence floods in the future? Important notes: – please fully read the requirements, and comprehensively read the articles I attached.- include an Excel sheet that include the statistics you found, like the excel document attached below. – Use APA format for citation. – Just put brief points in the slides and the description in the comments below each slide, Imagine yourself the presenter, you would put the desertion in the comments of each slide using simple words so you can read read easily. – Please fully cover the whole aspects of the question, its to confirm the full understanding of statistics – Please go through the two videos in the two links below, to give more understanding of statistics: 1- https://youtu.be/YHXadaW_lso 2- https://youtu.be/HeKyTGZlLhI This assignment is more likely to deliver the understanding of statistics methods to do a research about my topic of research, and to know how analyse it. This assignment is very IMPORTANT. Also think about my topic of research, please comprehensively read my research, and Remember that my research question is: What preventive methods that Saudi Arabia should use to avoid the occurrence of floods in the future, the targeted population are three cities in Saudi including Makkah, Jeddah, and Ryadh. So think about a statistics aspect that is related and important for the research question. Please deliver a high quality, this a vital assignment, so please focus on the requirements. Don’t define statistics or so, just start directly to dothe statistics of the topic of research. Use a reliable resource to find the information you need for statistics like https://www.stats.gov.sa/en/node .
descriptive_statistics.pdf

inferential_statistics.pdf

descriptive_vs_inferential_statistics__1_.doc

titanic3_demographic_raw_from_vanderbilt.xls

managing_floods_in_saudi_arabia_in_the_future.doc

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Descriptive v. Inferential Statistics
Descriptive are those which describe and summarize data.
Percentages
mean median and mode
range and variance
standard deviation
Inferential statistics allow one to make inferences from the sample to the general
population. These statistics measure probability which aid in drawing conclusions.
t tests
F tests
r tests
Nominal Data – giving a number to non-numerical information 1=male, 2=female
key is that there is no numerical value to the data, can’t compute mean etc…
Acts more as a code of information.
Ordinal Data – indicates a greater or lesser degree of something. Likert scale
Interval Data – has a logical sequence but does measure something i.e.; reaction times
Ratio Data – measurements of most physical variables qualify as ratio data: length,
weight, time, voltage, pressure, and velocity. The game speed activity as example.
Parametric v. Nonparametric Data
Nominal and ordinal data are nonparametric,
Interval and ratio data are parametric.
Descriptive v. Inferential
Descriptive Statistics – analysis by description
Inferential Statistics – used to infer whether the data can be taken to occur in the more
general target population.
Descriptive Statistics Central tendency – mean median and the mode
relative position – can use a range of scores or use Standard Deviation
Standard Deviation problem 1st Example
What does it mean? If two distributions had a mean of 25, but one had a standard
deviation of 7 and the other 3. We would know that the second had a more
homogeneous. It helps us understand it as an average of the deviations from the mean.
The mean tells us the single best point for summarizing an entire distribution or the
central tendency, while a standard deviation tells us how much, on the average the scores
deviate from that mean. An indicator of our degree of error.
2nd Example: Zephyrs v. Zebras – Let’s take an example
Inferential Statistics – is the data significant to support the statement that we think it can
be generalized to a larger population.
Three types of inferential stats-observed differences are significantly different
-two scores to find the associated strength
-variation of two scores
Significance of difference
ROXO
RO O
The key here in this classical design is to assure that the change in the control v. the
experimental group is caused by the independent variable. Shows a cause and effect to a
statistical significance.
The key is also to rule out a chance occurrence that the change would have happened
anyway.
The laws of probability say that 5 out of 100 of the change associated with chance
occurrence is acceptable. Anything better than that is significant to say that the change is
due to the independent variable. Many scientists use a 1 out of 100 as chance occurrence
This is expressed in terms of p<.05 T-tests used to compare the mean scores of two groups Single sample T-test We have a hypothesis that states given a group of paramedic students, those who attend a special workshop on the elderly will develop positive attitudes towards the elderly. ROXO RO O One group has the training the other does not. The post test will determine whether attitudes have significantly changed regarding the students. T scores will tell Example: Tests for Correlation What is a P value? Why do we need statistical calculations? When analyzing data, your goal is simple: You wish to make the strongest possible conclusion from limited amounts of data. To do this, you need to overcome two problems: • Important differences can be obscured by biological variability and experimental imprecision. This makes it hard to distinguish real differences from random variability. • The human brain excels at finding patterns, even from random data. Our natural inclination (especially with our own data) is to conclude that differences are real, and to minimize the contribution of random variability. Statistical rigor prevents you from making this mistake. Statistical analyses are most useful when you are looking for differences that are small compared to experimental imprecision and biological variability. If you only care about large differences, you may follow these aphorisms: If you need statistics to analyze your experiment, then you’ve done the wrong experiment. If your data speak for themselves, don’t interrupt! But in many fields, scientists care about small differences and are faced with large amounts of variability. Statistical methods are necessary. Population vs. samples The basic idea of statistics is simple: you want to extrapolate from the data you have collected to make general conclusions. Statistical analyses are based on a simple model. There is a large population of data out there, and you have randomly sampled parts of it. You analyze your sample to make inferences about the population. Consider several situations: Quality control Sample: The items you tested. Population: The entire batch of items produced. Political polls Sample: The ones you polled. Population: All voters. Clinical studies Sample: Subset of patients who attended Tuesday morning clinic in August Population: All similar patients. Laboratory research Sample: The data you actually collected Population: All the data you could have collected if you had repeated the experiment many times the same way The logic of statistics assumes that your sample is randomly selected from the population, and that you only want to extrapolate to that population. This works perfectly for quality control. When you apply this logic to scientific data, you encounter two problems: • You don’t really have a random sample. It is rare for a scientist to randomly select subjects from a population. More often you just did an experiment a few times and want to extrapolate to the more general situation. It is sufficient that your data be representative of the population, and that the population be hypothetical. • You want to make conclusions that extrapolate beyond the population. The statistical inferences only apply to the population your samples were obtained from. Let’s say you perfonn an experiment in the lab three times. All the experiments used the same cell preparation, the same buffers, and the same equipment. Statistical inferences let you make conclusions about what would happen if you repeated the experiment many more times with that same cell preparation, those same buffers, and the same equipment. You probably want to extrapolate further to what would happen if someone else repeated the experiment with a different source of cells, freshly made buffer and different instruments. Statistics can’t help with this further extrapolation. You can use scientific judgment and common sense to make inferences that go beyond statistics. Statistical logic is only part of data interpretation. Assumption of independence It is not enough that your data are sampled from a population. Statistical tests are also based on the assumption that each subject (or each experimental unit) was sampled independently of the rest. The assumptions of independence is easiest to understand by studying counterexamples. • You are measuring blood pressure in animals. You have five animals in each group, and measure the blood pressure three times in each animal. You do not have 15 independent measurements, because the triplicate measurements in one animal are likely to be closer to each other than to measurements from the other animals. You should average the three measurements in each animal. Now you have five mean values that are independent of each other. • You have done a laboratory experiment three times, each time in triplicate. You do not have nine independent values. If you average the triplicates, you do have three independent mean values. • You are doing a clinical study, and recruit ten patients from an inner-city hospital and ten more patients from a suburban clinic. You have not independently sampled 20 subjects from one population. The data from the ten inner-city patients may be closer to each other than to the data from the suburban patients. You have sampled from two populations, and need to account for this in your analysis. This is a complicated situation, and you should probably contact a statistician. Confidence intervals Statistical calculations produce two kinds of results that help you make inferences about the populations from the samples. You’ve already learned about P values. The second kind of result is a confidence interval. 95% confidence interval of a mean Although the calculation is exact, the mean you calculate from a sample is only an estimate of the population mean. How good is the estimate? It depends on how large your sample is and how much the values differ from one another. Statistical calculations combine sample size and variability to generate a confidence interval for the population mean. You can calculate intervals for any desired degree of confidence, but 95% confidence intervals are used most commonly. If you assume that your sample is randomly selected from some population, you can be 95% sure that the confidence interval includes the population mean. More precisely, if you generate many 95% CI from many data sets, you expect the CI to include the true population mean in 95% of the cases and not to include the true mean value in the other 5%. Since you don’t know the population mean, you’ll never know for sure whether or not your confidence interval contains the true mean. Other situations When comparing groups, calculate the 95% confidence interval for the difference between the population means. Again interpretation is straightforward. If you accept the assumptions, there is a 95% chance that the interval you calculate includes the true difference between population means. Methods exist to compute a 95% confidence interval for any calculated statistic, for example the relative risk or the best-fit value in nonlinear regression. The interpretation is the same in all cases. If you accept the assumptions of the test, you can be 95% sure that the interval contains the true population value. Or more precisely, if you repeat the experiment many times, you expect the 95% confidence interval will contain the true population value in 95% of the experiments. Why 95%? There is nothing special about 95%. It is just convention that confidence intervals are usually calculated for 95% confidence. In theory, confidence intervals can be computed for any degree of confidence. If you want more confidence, the intervals will be wider. If you are willing to accept less confidence, the intervals will be narrower. pclass survived name 1 1 Allen, Miss. Elisabeth Walton 1 1 Allison, Master. Hudson Trevor 1 0 Allison, Miss. Helen Loraine 1 0 Allison, Mr. Hudson Joshua Creighton 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 1 1 Anderson, Mr. Harry 1 1 Andrews, Miss. Kornelia Theodosia 1 0 Andrews, Mr. Thomas Jr 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) 1 0 Artagaveytia, Mr. Ramon 1 0 Astor, Col. John Jacob 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) 1 1 Aubart, Mme. Leontine Pauline 1 1 Barber, Miss. Ellen "Nellie" 1 1 Barkworth, Mr. Algernon Henry Wilson 1 0 Baumann, Mr. John D 1 0 Baxter, Mr. Quigg Edmond 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) 1 1 Bazzani, Miss. Albina 1 0 Beattie, Mr. Thomson 1 1 Beckwith, Mr. Richard Leonard 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 1 1 Behr, Mr. Karl Howell 1 1 Bidois, Miss. Rosalie 1 1 Bird, Miss. Ellen 1 0 Birnbaum, Mr. Jakob 1 1 Bishop, Mr. Dickinson H 1 1 Bishop, Mrs. Dickinson H (Helen Walton) 1 1 Bissette, Miss. Amelia 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan 1 0 Blackwell, Mr. Stephen Weart 1 1 Blank, Mr. Henry 1 1 Bonnell, Miss. Caroline 1 1 Bonnell, Miss. Elizabeth 1 0 Borebank, Mr. John James 1 1 Bowen, Miss. Grace Scott 1 1 Bowerman, Miss. 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Sara Rebecca female Compton, Mr. Alexander Taylor Jr male Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll) female Cornell, Mrs. Robert Clifford (Malvina Helen Lamson) female Crafton, Mr. John Bertram male Crosby, Capt. Edward Gifford male Crosby, Miss. Harriet R female Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead) female Cumings, Mr. John Bradley male Cumings, Mrs. John Bradley (Florence Briggs Thayer) female Daly, Mr. Peter Denis male Daniel, Mr. Robert Williams male Daniels, Miss. Sarah female Davidson, Mr. Thornton male Davidson, Mrs. Thornton (Orian Hays) female Dick, Mr. Albert Adrian male Dick, Mrs. Albert Adrian (Vera Gillespie) female Dodge, Dr. Washington male Dodge, Master. Washington male Dodge, Mrs. Washington (Ruth Vidaver) female Douglas, Mr. Walter Donald male Douglas, Mrs. Frederick Charles (Mary Helene Baxter) female Douglas, Mrs. Walter Donald (Mahala Dutton) female Duff Gordon, Lady. 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Mabel Helen Fortune, Mr. Charles Alexander Fortune, Mr. Mark Fortune, Mrs. Mark (Mary McDougald) Francatelli, Miss. Laura Mabel Franklin, Mr. Thomas Parham Frauenthal, Dr. Henry William Frauenthal, Mr. Isaac Gerald Frauenthal, Mrs. Henry William (Clara Heinsheimer) Frolicher, Miss. Hedwig Margaritha Frolicher-Stehli, Mr. Maxmillian Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli) Fry, Mr. Richard Futrelle, Mr. Jacques Heath Futrelle, Mrs. Jacques Heath (Lily May Peel) Gee, Mr. Arthur H Geiger, Miss. Amalie Gibson, Miss. Dorothy Winifred Gibson, Mrs. Leonard (Pauline C Boeson) Giglio, Mr. Victor Goldenberg, Mr. Samuel L Goldenberg, Mrs. Samuel L (Edwiga Grabowska) Goldschmidt, Mr. George B Gracie, Col. Archibald IV Graham, Miss. 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