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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Intro…
Why study statistics?
Make decision without complete informations
Understanding population, sample
Parameter, statistic
Descriptive and inferential statistics
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glossary
A population is the collection of all items of interest or
under investigation
N represents the population size
A sample is an observed subset of the population
n represents the sample size
A parameter is a specific characteristic of a population
Mean, Variance, Standard Deviation, Proportion, etc.
A statistic is a specific characteristic of a sample
Mean, Variance, Standard Deviation, Proportion, etc.
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Population vs. Sample
Population
a b
Sample
cd
b
ef gh i jk l m n
gi
o p q rs t u v w
x y
c
o
z
n
r
u
y
Values calculated using
population data are called
parameters
Values computed from sample
data are called statistics
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Examples of Populations
Incomes 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 statistics
Collecting, summarizing, and processing data to
transform data into information
Inferential statistics
Provide 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 =
X
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n
i
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
Inference is the process of drawing conclusions or making decisions about a
population based on sample results
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The Decision Making Process
Decision
Knowledge
Experience, Theory,
Literature, Inferential
Statistics, Computers
Information
Descriptive Statistics,
Probability, Computers
Begin Here:
Identify the
Problem
Data
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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
Slope
= 0.10977
Intercept
= 98.248
house price  98.24833  0.10977 (square feets)
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Bagaimana mendapatkan persamaan
garis regresi ?
Next
Bawa kalkulator setiap perkuliahan regresi
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