Ipek Deveci Kocakoç

Terimler listesi

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Category: Dersler

Published on Thursday, 29 March 2012 15:26

Written by Super User

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Important terms for Sections A and C in Mid-Term exam

 

Term in English	Term in Turkish	Definition in English
variable
qualitative data
attribute
nominal variable
ordinal variable
binary variable
quantitative data
discrete variable
continuous variable
descriptive statistics
census
parameter
sample
statistics
mean
median
standard deviation
root mean square
variance
range
frequency
cumulative frequaency
absolute frequency
relative frequency
cumulative relative frequency
histogram
skewed distribution
probability
event
a-priori probability
empirical probability

 

Mesleki Yabancı Dil-I

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Category: Dersler

Published on Wednesday, 22 February 2012 11:38

Written by Super User

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ÖNEMLİ DUYURULAR:

 

• Dersi alttan alanlar için ödev: Dersi alttan alıp başka dersi ile çakıştığını transkripti ve ders programı ile kanıtlayan öğrenciler, derste alamadıkları artılar için ödev getirebilirler. Ödev olarak aşağıdaki soruların el yazısı ile kendiniz tarafından cevaplanmış halini final sınavında teslim edebilirsiniz. 
• Tüm öğrencilerin Ödev kitapçıklarını da (boş ta olsa) final sınavında imza karşılığı teslim etmesi gerekmektedir.
• Vize sınavının A ve C kısımları için Terimler listesi 

 

Videolar:

http://www.youtube.com/watch?v=2gVIgw9NYZo
http://www.youtube.com/watch?v=hZxnzfnt5v8
http://www.youtube.com/watch?v=daIb2VF1i3M
http://www.youtube.com/watch?v=Tp53kvPL9W4

Dersin notları İlkem Fotokopidedir. Ödevlerin takibi için bir ödev kitapçığını da fotokopiden almanız gerekmektedir.

Derste %30 puanlık zorunlu ödev uygulaması yapılmaktadır. Detaylı bilgi için ders notlarının ilk sayfasını okuyunuz.

Derste işlenen sorular aşağıda verilmiştir. Soruların aynısı kesinlikle sınavlarda çıkmayacaktır.

MYD-I-Haftalık Ödevler için tıklayınız.

Faydalanabileceğiniz sözlükler:

http://www.freelanceresearcher.net/liste.asp   --> İstatistik terimleri sözlüğü

http://translate.google.com.tr/ --> İngilizceden Türkçeye çeviri (Pek güvenmeyiniz)

www.seslisozluk.com

www.zargan.com

www.sozluk.net

 

Questions

 

Chapter 1

 

1. What does “variable” mean? Give an example.
2. What are the key learning skills for Chapter1?
3. What does “qualitative data” mean?
4. What is an attribute?
5. Give some examples of qualitative data.
6. What are the scales of categorical variables?
7. What does “nominal variable” mean? Give examples.
8. What does “ordinal variable” mean? Give examples.
9. When must the analyst be cautious with ordinal scales?
10. What does “binary variable” mean? Give examples.
11. What does “quantitative data” mean?
12. What does “discrete variable” mean? Give examples
13. What does “continuous” mean? Give examples.
14. How do we convert a categorical variable to a quantitative scale?
15. How do we approximate discrete variables in analysis?

 

Chapter 2

 

 

 

1. What are descriptive statatistics?
2. What are the key learning skills for Chapter2?
3. What is population data? Give examples.
4. What is “census”?
5. Why do we collect data?
6. What is parameter?
7. What is statistics?
8. Why do we use a statistic?
9. What is the difference between a statistic and a parameter?
10. What are the measures of central tendency?
11. Explain “mean”, give examples.
12. What is “median”? how do we calculate it?
13. Give an example to median.
14. Why do we use median?
15. What are the measures of variability?
16. What is Standard deviation and how do we calculate it?
17. Which notation do we use for Standard deviation?
18. What is variance?
19. What is range?

Chapter 3

 

• What is frequency and cumulative frequency?
• What are the key learning skills for Chapter3?
• Explain the following terms about frequency: Absolute, relative, cumulative, cumulative relative.
• Explain the example about the dice.
• What is a histogram? Why do we use it?
• What are the some common shapes of histograms? Give examples.
• Explain the figure about histogram shapes (page 12).
• Explain discrete histogram.
• How do we draw a continuous histogram? (2+)
• How do we interpret a continuous histogram

 

Chapter 4

 

1. What is probability? Give an example.
2. What are the key learning skills for this chapter?
3. What is probability?
4. Is the event always a desirable one in probability?
5. What is a priori probability?
6. What is empirical probability?
7. How do we obtain a priori probability?
8. Give an example for a priori probability.
9. Give an example for empirical probability.
10. What is a common method for empirically estimating probabilities?
11. What is the relation of probability to risk? Give an example.
12. Explain claim-hypothesis relation.
13. What is Type-I error?
14. What is Type-II error?
15. Explain the jury trial analogy to Type-I an Type-II errors.
16. How do we control the probability of Type-I error?
17. How do we control the probability of Type-II error?
18. What is p-value?
19. Give an example to p-value.

Chapter 5

 

1. What does Normal distribution represent?
2. Where do we use normal distribution?
3. What does it mean when we can’t find a normal distribution when studying a continuous process?
4. What are the key learning skills for this chapter?
5. What are the properties of normal distrubution?
6. What is the first thing to do to estimate probabilities from normal distribution?
7. What do Z scores do? (whole paragraph, 2+)
8. What happens by standardizing data? Give example.

Chapter 6

 

1. What are the key learning skills for this chapter?
2. Explain general regression equation.
3. What does simple linear regression do?
4. What is the most common method to determine the best fit?
5. What can you use alternatively?
6. Interpret the example about linear regression.
7. What is Pearson correlation coefficient? How do we calculate it?
8. How do we interpret the correlation coefficient r?
9. Why do we use scatter plots?
10. What does a strong positive correlation mean?
11. What does a strong negative correlation mean?
12. What are the important issues to consider when drawing conclusions based on correlation coefficients? (1 + for each item)
13. What does multiple regression do?
14. Why should we be cautious when using multiple regression?

THE END of the BOOK!!!

MYD-I-Haftalık Ödevler

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Category: Dersler

Published on Wednesday, 22 February 2012 12:03

Written by Super User

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Haftalık Ödevleri ödev kitapçığına cevaplayıp zamanında (ders saatinde) getiriniz.

1. hafta: 22.02.12

Qualitative data involves assigning non-numerical items into groups or categories. Qualitative data also are referred to as categorical data. The qualitative characteristic or classification group of an item is an attribute. Categorical variables are typically assigned attributes using a nominal, ordinal, or binary scale.

Nominal variables are categorical variables that have three or more possible levels with no natural ordering.

Ordinal variables are categorical variables that have three or more possible levels with a natural ordering, such as strongly disagree, disagree, neutral, agree, and strongly agree.

Binary variables are categorical variables that have two possible levels (e.g., yes/no).

 2. hafta - 29.02.12

 

A population data set includes all items of the set, such as the height of every person in the United States, or the volume of every can of soda pop that a manufacturer produces. If the desired information is available for all items in the population, we have what is referred to as a census. In practice, we rarely have a complete set of data. We usually collect data in samples, such as the volumes of the last thirty cans of pop.

3. hafta - 07.03.12

 

To compute the population standard deviation, we use the population mean and divide by n instead of n-1. In practice, the population standard deviation is rarely used because the true population mean is usually unknown. The use of the sample standard deviation is particularly important for smaller sample sizes. However, as the sample size gets large (say n > 100), the difference between dividing by n versus n-1 may become negligible. The typical notation used to represent the sample standard deviation is

S; the Greek letter s is used to represent the population standard deviation. The terms, S or sˆ , represent the estimate of the population standard deviation.

 

4. hafta - 14.03.12

 

First, arrange the data into frequency or bin ranges of equal width. The selection of the number and width of the bins (frequency ranges) is dependent on the analyst. For continuous data, a general rule of thumb is to set the number of bins equal to the square root of the number of samples (rounded to nearest whole number). To obtain the bin width, divide the range of the data set by the number of bins (rounded to desired precision of measurement data). This example has 100 samples and a range of 0.23. Thus, an analyst might create 10 bins (=sqrt(100)) of width 0.02 mm (0.23/10 = 0.02). In this example, the value 25.34 was chosen as the starting point because relatively few values are below it.

5. hafta:

http://www.youtube.com/watch?v=2gVIgw9NYZo
http://www.youtube.com/watch?v=hZxnzfnt5v8
http://www.youtube.com/watch?v=daIb2VF1i3M

Adreslerindeki videoları dinleyerek aşağıdaki soruları cevaplayınız:

1. Write down the terms (both in English and Turkish,with descriptions if possible) in all videos that :

a. You already know

b. Are new to you

2. Write down what exactly is spoken for at least 30 seconds of any video stream. (Write the name of the video you choose)

3. What did you learn from these videos?

Vizeden sonraki ödevler:

1. hafta:

 

p-values and statistical significance

 

When conducting a statistical analysis, the p-value is used to represent the probability that no difference exists (for example, two machines are not producing statistically different mean outputs). A common method for determining significance in a statistical comparison is to conclude a difference exists if the p-value is less than the alpha error level.

 

For example, suppose you determine in a comparison of two bottle-filling machines that the probability that the two means are not different is 0.24. Assuming alpha = 0.05, you would conclude that the two means are not different. However, if the p-value is 0.003, you would conclude that the means

are different. In this example, you are 99.7% confident that you are making the correct decision.

2. hafta:

 

Z scores transform data into the standard cumulative normal distribution whose mean = 0, and variance (

s2

) = 1. Z-scores provide a mapping from a distribution of some variable to a standardized scale. These mappings reflect the difference in terms of number of standard deviations away from the mean. If the mean of a process = 4 mm and the standard deviation = 1, then an observed value of 1 could also be represented as –3*standard deviation from the mean. For this example, a Z = -3 is equivalent to an actual observation of 1 (where Z = – 3*standard deviation away from the mean). 

3. hafta

When drawing conclusions based on correlation coefficients, several important items must be considered: 

·  Correlation coefficients only measure linear relationships. A meaningful nonlinear relationship can exist even if the correlation coefficient is 0.

·  Correlation does NOT always indicate cause and effect. One should not conclude that changes to one variable cause changes in another. Properly controlled experiments are needed to verify that a correlation relationship indicates causation. 

A correlation coefficient is very sensitive to extreme values. A single value that is very different from the others in a data set can change the value of the coefficient a great deal. In the example below, the correlation is 0.9, but the scatter plot suggests that an outlier more likely explains the relationship that the predictor variable. If you removed the outlier value, the correlation between these two variable would drop to 0.1 over the smaller range of X.

 

 

 

MVS-II

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Category: Dersler

Published on Thursday, 16 February 2012 11:34

Written by Super User

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Multivariate Statistics – II Syllabus – Spring, 2010-2011

 

Main Reference: 

 

Multivariate Data Analysis ,Joseph F. Hair (Author), William C. Black (Author), Barry J. Babin (Author), Rolph E. Anderson (Author), Pearson Education, 7th Edition, 2009.

 

 Course syllabus:

 

Data screening

 

Bivariate correlation and simple linear regression

 

Multiple regression

 

Logistic regression

 

Discriminant analysis

 

Univariate comparisons of means

 

Article discussions

 

Mid-Term

 

MANOVA- comparing two groups

 

MANOVA- comparing three or more groups

 

MANOVA- two way factorial

 

Principle components and factor analysis

 

 

Useful links:

 

 

 

Book’s website: http://www.mvstats.com/

 

Data sets: http://research.ed.asu.edu/multimedia/DrB/Default.htm

 

SPSS :http://core.ecu.edu/psyc/wuenschk/SPSS/SPSS-MV.htm

 

Summary: http://www.utdallas.edu/~herve/Abdi-MultivariateAnalysis-pretty.pdf

 

Notes: http://faculty.chass.ncsu.edu/garson/PA765/statnote.htm