ANALISIS REGRESI
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Deskripsi matakuliah Mempelajari :
Analisis regresi linear sederhana
Analisis regresi linear berganda
Asumsi-asumsi dalam regresi
Estimasi koefisien dan persamaan regresi
Inferensi dan interpretasi dalam regresi
Analisis variansi pada regresi
Pendekatan matriks dalam analisis regresi
Jumlah kuadrat ekstra
Analisis korelasi
Regresi lain (regresi polinomial, regresi dummy,regresi logistik, regresi PLS)
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Referensi Neter, John. 1990. Applied Linear Statistical Models :
Regression, Analysis of Variance, and Experimental Design . Irwin : Boston
Model linier terapan I dan II (terjemahan)
Sumantri, B. (1997). Model Linear Terapan, Buku I. Jurusan Statistika: FMIPA IPB
Sumantri, B. (1997). Model Linear Terapan, Buku II. Jurusan Statistika: FMIPA IPB
Myers, R.H. (1996). Classical and Modern Regression with Applications. Boston : PWS-KENT Publishing Company
Sembiring. (1995). Analisis Regresi , Bandung : ITB
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Kontrak perkuliahan
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Why study statistics?
Make decision without complete informations
Understanding population, sample
Parameter, statistic
Descriptive and inferential statistics
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Intro…
glossaryA population is the collection of all items of interest or
under investigationN represents the population size
A sample is an observed subset of the populationn represents the sample size
A parameter is a specific characteristic of a populationMean, Variance, Standard Deviation, Proportion, etc.
A statistic is a specific characteristic of a sampleMean, Variance, Standard Deviation, Proportion, etc.
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Population vs. Sample
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a b c d
ef gh i jk l m n
o p q rs t u v w
x y z
Population Sample
b c
g i n
o r u
y
Values calculated using population data are called parameters
Values computed from sample data are called statistics
Examples of PopulationsIncomes of all families living in yogyakarta
All women with pregnancy problem.
Grade point averages of all the students in your university
…
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Random sampling
Simple random sampling is a procedure in which
each member of the population is chosen strictly by chance,
each member of the population is equally likely to be chosen, and
every possible sample of n objects is equally likely to be chosen
The resulting sample is called a random sample
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Descriptive and Inferential Statistics
Two branches of statistics:
Descriptive statisticsCollecting, summarizing, and processing data to
transform data into information
Inferential statisticsProvide the bases for predictions, forecasts, and
estimates that are used to transform information into
knowledge and decision
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Descriptive Statistics
Collect data
e.g., Survey
Present data
e.g., Tables and graphs
Summarize data
e.g., Sample mean =
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iX
n
Inferential Statistics
Estimation
e.g., Estimate the population mean weight using the sample mean weight
Hypothesis testing
e.g., Test the claim that the population mean weight is 120 pounds
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Inference is the process of drawing conclusions or making decisions about a population based on sample results
The Decision Making Process
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Begin Here:
Identify the Problem
Data
Information
Knowledge
Decision
Descriptive Statistics,Probability, Computers
Experience, Theory,Literature, InferentialStatistics, Computers
Independent and Dependent Variable Example case:
A real estate agent wishes to examine the relationship between the selling price of a house ($1000s) and its size(measured in square feets)Dependent variable (Y) = house price in $1000s
Independent variable (X) = house’size
Dependent variable : response variable
Independent variable : predictor variable
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Sample Data for House Price Model
House Price in $1000s(Y)
Square feets (X)
245 1400
312 1600
279 1700
308 1875
199 1100
219 1550
405 2350
324 2450
319 1425
255 1700
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Scatter plot
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Graphical Presentation
House price model: scatter plot and regression line
s)f (square 0.10977 98.24833 price house eet
Slope = 0.10977
Intercept = 98.248
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Bagaimana mendapatkan persamaan garis regresi ?Next
Bawa kalkulator setiap perkuliahan regresi
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