– 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

Unformatted Attachment Preview

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.
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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. Margaret Edith
Graham, Mr. George Edward
Graham, Mrs. William Thompson (Edith Junkins)
Greenfield, Mr. William Bertram
Greenfield, Mrs. Leo David (Blanche Strouse)
Guggenheim, Mr. Benjamin
Harder, Mr. George Achilles
Harder, Mrs. George Achilles (Dorothy Annan)
Harper, Mr. Henry Sleeper
Harper, Mrs. Henry Sleeper (Myna Haxtun)
Harrington, Mr. Charles H
Harris, Mr. Henry Birkhardt
Harris, Mrs. Henry Birkhardt (Irene Wallach)
Harrison, Mr. William
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