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Mesleki Yabanc覺 Dil-I

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Category: Dersler
Published on Wednesday, 22 February 2012 11:38
Written by Super User
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NEML襤 DUYURULAR:

  • Dersi alttan alanlar i癟in 繹dev: Dersi alttan al覺p baka dersi ile 癟ak覺t覺覺n覺 transkripti ve ders program覺 ile kan覺tlayan 繹renciler, derste alamad覺klar覺 art覺lar i癟in 繹dev getirebilirler. dev olarak aa覺daki sorular覺n el yaz覺s覺 ile kendiniz taraf覺ndan cevaplanm覺 halini final s覺nav覺nda teslim edebilirsiniz.
  • T羹m 繹rencilerin dev kitap癟覺klar覺n覺 da (bo ta olsa) final s覺nav覺nda imza kar覺l覺覺 teslim etmesi gerekmektedir.
  • Vize s覺nav覺n覺n A ve C k覺s覺mlar覺 i癟in 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 notlar覺 襤lkem Fotokopidedir. devlerin takibi i癟in bir 繹dev kitap癟覺覺n覺 da fotokopiden alman覺z gerekmektedir.

Derste %30 puanl覺k zorunlu 繹dev uygulamas覺 yap覺lmaktad覺r. Detayl覺 bilgi i癟in ders notlar覺n覺n ilk sayfas覺n覺 okuyunuz.

Derste ilenen sorular aa覺da verilmitir. Sorular覺n ayn覺s覺 kesinlikle s覺navlarda 癟覺kmayacakt覺r.

MYD-I-Haftal覺k devleri癟in t覺klay覺n覺z.

Faydalanabileceiniz s繹zl羹kler:

http://www.freelanceresearcher.net/liste.asp --> 襤statistik terimleri s繹zl羹羹

http://translate.google.com.tr/--> 襤ngilizceden T羹rk癟eye 癟eviri (Pek g羹venmeyiniz)

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 cant 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!!!