This document provides information about stepwise multiple regression, including:
1) Stepwise regression selects variables for inclusion in the model based on their statistical contribution to explaining variance in the dependent variable.
2) It aims to find the most parsimonious set of predictors that effectively predict the dependent variable by adding variables one at a time.
3) Validation is necessary when using stepwise regression to ensure the model developed on the training data generalizes to new data. 75/25 cross-validation is recommended.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Calculation of the correlation between height and weight of the studentsVidya Kalaivani Rajkumar
The document describes calculating the correlation between the height and weight of 23 students. It explains that correlation describes the likelihood that a change in one variable (e.g. height) will cause a proportional change in the other (weight). It provides the formula used to calculate the correlation coefficient, which ranges from +1 (perfect positive correlation) to -1 (perfect negative correlation). The result of calculating the correlation coefficient between the heights and weights of the students is presented, but the value is not provided.
This document provides an introduction to Poisson regression models for count data. It outlines that Poisson regression can be used to model count variables that have a Poisson distribution. A simple equiprobable model is presented where the expected count is equal across all categories. This equiprobable model establishes a null hypothesis that can be tested using likelihood ratio or Pearson's test statistics. Residual analysis is also discussed. Finally, the document introduces how a covariate can be added to a Poisson regression model to establish relationships between the count variable and explanatory variables.
This document provides an overview of analysis of variance (ANOVA). It describes how ANOVA was developed by R.A. Fisher in 1920 to analyze differences between multiple sample means. The document outlines the F-statistic used in ANOVA to compare between-group and within-group variations. It also describes one-way and two-way classifications of ANOVA and provides examples of applications in fields like agriculture, biology, and pharmaceutical research.
This document provides information about stepwise multiple regression, including:
1) Stepwise regression selects variables for inclusion in the model based on their statistical contribution to explaining variance in the dependent variable.
2) It aims to find the most parsimonious set of predictors that effectively predict the dependent variable by adding variables one at a time.
3) Validation is necessary when using stepwise regression to ensure the model developed on the training data generalizes to new data. 75/25 cross-validation is recommended.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
Normal or skewed distributions (descriptive both2) - Copyright updatedKen Plummer
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Calculation of the correlation between height and weight of the studentsVidya Kalaivani Rajkumar
The document describes calculating the correlation between the height and weight of 23 students. It explains that correlation describes the likelihood that a change in one variable (e.g. height) will cause a proportional change in the other (weight). It provides the formula used to calculate the correlation coefficient, which ranges from +1 (perfect positive correlation) to -1 (perfect negative correlation). The result of calculating the correlation coefficient between the heights and weights of the students is presented, but the value is not provided.
This document provides an introduction to Poisson regression models for count data. It outlines that Poisson regression can be used to model count variables that have a Poisson distribution. A simple equiprobable model is presented where the expected count is equal across all categories. This equiprobable model establishes a null hypothesis that can be tested using likelihood ratio or Pearson's test statistics. Residual analysis is also discussed. Finally, the document introduces how a covariate can be added to a Poisson regression model to establish relationships between the count variable and explanatory variables.
This document provides an overview of analysis of variance (ANOVA). It describes how ANOVA was developed by R.A. Fisher in 1920 to analyze differences between multiple sample means. The document outlines the F-statistic used in ANOVA to compare between-group and within-group variations. It also describes one-way and two-way classifications of ANOVA and provides examples of applications in fields like agriculture, biology, and pharmaceutical research.
This document provides an introduction to various regression analysis techniques used in chemometrics, including partial least squares regression (PLSR), principal component regression (PCR), simple linear regression, and multiple linear regression. PLSR can be used to relate two data matrices and analyze data with many variables, while PCR reduces standard errors in regression estimates. Examples of applications in chemistry, medicine, food research, and pharmacology are given. Deming regression is described as a technique for fitting a line to data where both variables have measurement error.
The document provides information about regression analysis and calculating the coefficient of determination. It includes:
1) Instructions on how to perform a regression analysis using a calculator to find the least squares regression line, correlation coefficient, and residual plot from sample data.
2) An explanation of the coefficient of determination as a measure of how much variability in the variable y can be explained by its linear relationship with variable x.
3) A calculation example finding the coefficient of determination to be 0.83 for a dataset relating height and shoe size, meaning approximately 83% of the variation in shoe size can be explained by height.
This document discusses statistical inference and its two main types: estimation of parameters and testing of hypotheses. Estimation of parameters has two forms: point estimation, which provides a single numerical value as an estimate, and interval estimation, which expresses the estimate as a range of values. Point estimation involves calculating estimators like the sample mean to estimate population parameters. Interval estimation provides a interval rather than a single point as the estimate. Statistical inference uses samples to draw conclusions about unknown population parameters.
This document provides an overview of simple linear regression analysis. It discusses estimating regression coefficients using the least squares method, interpreting the regression equation, assessing model fit using measures like the standard error of the estimate and coefficient of determination, testing hypotheses about regression coefficients, and using the regression model to make predictions.
This document discusses different types of graphs used to represent frequency distributions: histograms, frequency polygons, and ogives. It provides examples and instructions for constructing each graph type. Histograms use vertical bars to represent frequencies, frequency polygons connect points plotted for class midpoints, and ogives show cumulative frequencies. The document also discusses relative frequency graphs and common distribution shapes like bell-shaped, uniform, and skewed. It assigns practice constructing different graph types from example data.
very detailed illustration of Log of Odds, Logit/ logistic regression and their types from binary logit, ordered logit to multinomial logit and also with their assumptions.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This document provides an overview of multiple regression analysis. It defines multiple regression, explains how to interpret regression coefficients and outputs, and discusses best practices for variable selection and assessing assumptions. Examples are provided on how to conduct multiple regression in SPSS to analyze customer survey data from two restaurants. Advanced topics like multicollinearity and dummy variables are also mentioned.
The test statistic is some statistic that may be computed from the data of the sample. The test statistic serves as a decision maker, since the decision to reject or not to reject the null hypothesis depends on the magnitude of the test statistic
Binary OR Binomial logistic regression Dr Athar Khan
Binary logistic regression can be used to model the relationship between predictor variables and a binary dependent variable. The document discusses using logistic regression to predict the likelihood of clients terminating counseling early based on gender, income level, avoidance of disclosure, and symptom severity. The full model was statistically significant and correctly classified 84.4% of cases. Avoidance of disclosure and symptom severity significantly predicted early termination, while gender and income level were not significant predictors.
The document discusses using a chi-square test to analyze the relationship between obesity and diabetes. It presents observed data showing higher diabetes rates among overweight individuals. The null hypothesis is that there is no association between weight and diabetes risk. Calculations yield a chi-square value above the critical value, leading to rejection of the null hypothesis and conclusion that overweight individuals have a significantly higher diabetes rate than normal weight individuals.
This document provides an introduction to the statistical concept of kurtosis. It defines kurtosis as a measure of the peakedness of a distribution that indicates how concentrated data is around the mean. There are three main types of kurtosis: leptokurtic distributions have higher peaks; platykurtic have lower peaks; and mesokurtic have normal peaks. Methods for calculating kurtosis include percentile measures and measures based on statistical moments. An example calculation demonstrates a leptokurtic distribution with a kurtosis value greater than 3. SPSS syntax for computing kurtosis from data is also presented.
This document provides an introduction to various regression analysis techniques used in chemometrics, including partial least squares regression (PLSR), principal component regression (PCR), simple linear regression, and multiple linear regression. PLSR can be used to relate two data matrices and analyze data with many variables, while PCR reduces standard errors in regression estimates. Examples of applications in chemistry, medicine, food research, and pharmacology are given. Deming regression is described as a technique for fitting a line to data where both variables have measurement error.
The document provides information about regression analysis and calculating the coefficient of determination. It includes:
1) Instructions on how to perform a regression analysis using a calculator to find the least squares regression line, correlation coefficient, and residual plot from sample data.
2) An explanation of the coefficient of determination as a measure of how much variability in the variable y can be explained by its linear relationship with variable x.
3) A calculation example finding the coefficient of determination to be 0.83 for a dataset relating height and shoe size, meaning approximately 83% of the variation in shoe size can be explained by height.
This document discusses statistical inference and its two main types: estimation of parameters and testing of hypotheses. Estimation of parameters has two forms: point estimation, which provides a single numerical value as an estimate, and interval estimation, which expresses the estimate as a range of values. Point estimation involves calculating estimators like the sample mean to estimate population parameters. Interval estimation provides a interval rather than a single point as the estimate. Statistical inference uses samples to draw conclusions about unknown population parameters.
This document provides an overview of simple linear regression analysis. It discusses estimating regression coefficients using the least squares method, interpreting the regression equation, assessing model fit using measures like the standard error of the estimate and coefficient of determination, testing hypotheses about regression coefficients, and using the regression model to make predictions.
This document discusses different types of graphs used to represent frequency distributions: histograms, frequency polygons, and ogives. It provides examples and instructions for constructing each graph type. Histograms use vertical bars to represent frequencies, frequency polygons connect points plotted for class midpoints, and ogives show cumulative frequencies. The document also discusses relative frequency graphs and common distribution shapes like bell-shaped, uniform, and skewed. It assigns practice constructing different graph types from example data.
very detailed illustration of Log of Odds, Logit/ logistic regression and their types from binary logit, ordered logit to multinomial logit and also with their assumptions.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This document provides an overview of multiple regression analysis. It defines multiple regression, explains how to interpret regression coefficients and outputs, and discusses best practices for variable selection and assessing assumptions. Examples are provided on how to conduct multiple regression in SPSS to analyze customer survey data from two restaurants. Advanced topics like multicollinearity and dummy variables are also mentioned.
The test statistic is some statistic that may be computed from the data of the sample. The test statistic serves as a decision maker, since the decision to reject or not to reject the null hypothesis depends on the magnitude of the test statistic
Binary OR Binomial logistic regression Dr Athar Khan
Binary logistic regression can be used to model the relationship between predictor variables and a binary dependent variable. The document discusses using logistic regression to predict the likelihood of clients terminating counseling early based on gender, income level, avoidance of disclosure, and symptom severity. The full model was statistically significant and correctly classified 84.4% of cases. Avoidance of disclosure and symptom severity significantly predicted early termination, while gender and income level were not significant predictors.
The document discusses using a chi-square test to analyze the relationship between obesity and diabetes. It presents observed data showing higher diabetes rates among overweight individuals. The null hypothesis is that there is no association between weight and diabetes risk. Calculations yield a chi-square value above the critical value, leading to rejection of the null hypothesis and conclusion that overweight individuals have a significantly higher diabetes rate than normal weight individuals.
This document provides an introduction to the statistical concept of kurtosis. It defines kurtosis as a measure of the peakedness of a distribution that indicates how concentrated data is around the mean. There are three main types of kurtosis: leptokurtic distributions have higher peaks; platykurtic have lower peaks; and mesokurtic have normal peaks. Methods for calculating kurtosis include percentile measures and measures based on statistical moments. An example calculation demonstrates a leptokurtic distribution with a kurtosis value greater than 3. SPSS syntax for computing kurtosis from data is also presented.
1. 17. lokakuu 2015
0
Maapuolustuksen
uudistuva taistelutapa
ja sen johtaminen
• TAISTELUTILAN MUOKKAAMINEN ”JOS VASTUSTAJA VALMISTAUTUU
JALKAPALLOKENTÄLLE, NIIN ME OLEMME KAUKALOSSA KIEKKOILEMASSA”
• VASTUSTAJAN ENNAKOINTI ”KATSE YLÖS KIEKOSTA”
• JÄRJESTELMÄVAIKUTUS SYVÄLLÄ ALUEELLA ”KARVAUS YLHÄÄLTÄ ALKAEN”
• JATKUVA KOSKETUS SEKÄ SYNKRONOITU TULI JA LIIKE
” HYÖKKÄYSKUVIOT POHJAAVAT JOUKKUEEN, EI YKSILÖIDEN VAHVUUKSIIN”
• NOPEA TAISTELUKYVYN PALAUTUS ”HUOLTO PELAA VAIHTOPENKILLÄ”
2. 17. lokakuu 2015
1
Maavoimien uudistettu
taistelutapa – Mikä muuttuu?
• Tärkein muutos on luopuminen ”jäykähköstä” asemapuolustuksesta.
• Vastustajalle aiheutetaan sellaiset kalusto- ja henkilöstötappiot, että
se ei kykene jatkamaan hyökkäystä suunnitelmansa mukaisesti.
• Huolellisilla valmisteluilla, hajautetulla ryhmityksellä ja väistämällä
vihollisen tulivalmisteluja vähennetään omia tappioita.
• Taistelussa sovelletaan aktiivisesti ja monipuolisesti eri taistelulajeja
sissitoiminnan keinoista puolustukseen, hyökkäykseen ja viivytykseen
ratkaisun saavuttamiseksi.
• Asejärjestelmistä siirrytään taisteluosaston taistelujärjestelmään ja
sen kokonaisvaikutuksen käyttämiseen
3. 17. lokakuu 2015
2
54
14PR Vuokko
27
KUHMO
662
81
759
SUOMUSSALMI
44
27
S
1
2
RAATTEEN TIE
OS Kari
AKTIIVISUUS, LUOVUUS, NOPEA VOIMAN LISÄÄMINEN,
”MAASTOKELPOISUUS”, NOPEUS, VOIMIEN
NOPEA KESKITTÄMINEN, ALOITTEEN TEMPAAMINEN
JOHTI VOITTOIHIN
KUVAN KARTAT NYKY-SUOMEN KARTTOJA!
Uudistettu taistelutapa = avataan
ajattelua omalla taktiikan historialla
4. 17. lokakuu 2015
3
Maavoimien uudistetun
taistelutavan toteutus
Vaatimuksia omille joukoille ja toiminnalle
• Liikkuvuus
• Kyky toimia hajautettuna
• Salaaminen ja harhauttaminen
• Tiedustelu- ja valvonta
• Kyky voiman ajalliseen ja paikalliseen
keskittämiseen
• Kyky operatiiviseen tulenkäyttöön
• Kaukovaikuttaminen
• RSRAKH
• Ilmasta maahan kyky
• Erikoisjoukot
• Yhteisoperaatiot
Kyky vaikuttaa nopeasti viholliseen koko taistelualueella
Haluttuun loppuasetelmaan päästään
aiheuttamalla viholliselle kumulatiivisesti
kasvavat tappiot
UHKALÄHTÖISYYS
6. 17. lokakuu 2015
5
Jalkaväkikomppanian
suorituskykytavoite
• Jalkaväkikomppania kykenee hajautuneena
partioittain pimeässä maastossa säilyttämään
tuntuman vastustajaan, iskemään oikea
aikaisesti keskitetyllä tai hajautetulla tavalla ja
ryhmittymään tuntuma säilyttäen uuteen
iskuun.
• Komppanian rungolle on kyettävä liittämään
eri aselajijoukkoja noin 1.5 kertaisesti
perusvahvuuteensa verrattuna ja komppanian
on kyettävä käyttämään muualta tulevaa
tukea joko taisteluosaston tai alueellisen
puolustusjärjestelmän palveluiden kautta.
7. 17. lokakuu 2015
6
MAPU johtamisen suorituskykylisä
• RINNAKKAINEN SUUNNITTELU
• VAST VAIHTOEHTOJEN POISSULKEMINEN
• OPERATIIVINEN YLLÄTYS
• MAALITILANNEKUVA PARANTAA KOSKETUSTA
• KYKY JOHTAA OMIA VASTUSTAJAN RYHMITYKSESSÄ
• HAJAUTETUN TOIMINNAN KOORDINOINTI
• OPTIMOITU VAIKUTUS KUHUNKIN MAALIIN
• JÄRJESTELMÄVAIKUTUKSEN KÄYTTÖ JOUSTAVASTI
• HEIKKOIHIN KOHTIIN KOHDISTUVA VAIKUTUS
• ALUEHUOLLON SUUNNITTELU
• KRIITTISTEN SUORITUSKYKYJEN PALAUTTAMINEN
• VERKOSTO TUKEE TAISTELIJAA
8. 17. lokakuu 2015
7
Uudistusprosessin loppuasetelma
- uudistettu alueellisen puolustuksen taistelutapa
- uudistettu maavoimien joukkorakenne ja modulaariset kokoonpanot
- verkostopuolustus (koko yhteiskunnan voimavarojen hyödyntäminen ja
kansainvälinen yhteistyö)
- vaikuttamislähtöisyys (optimointi, kaukovaikuttaminen, operatiivinen tulenkäyttö)
- joustava johtamisrakenne ilman teknisiä ja toiminnallisia rajapintoja.
Haluttuun loppuasetelmaan päästään aiheuttamalla hyökkääjälle kumulatiiviset tappiot
Ryhmä t
B Joukkue
Komppania
Tukiasema
Tukiasema
Tukiasema
Parikaapeli
Liityntäverkon solmu
14 kpl
3*Jopa
A JoukkueValokaapelit
ja linkit
MATI op-net
verkon
palvelinhotellissa
MATI
Sanomaliikenne on läpi koko maapuolustuksen
TST-johtoverkko ja
MATI TSTJ
MAPU-liityntäverkko ja
MATI 2
ITVJ liityntä- verkko ja
LEIJONA 3
TST-osastolla on itsenäinen johtamiskyky
Ev TFo
Esikunta
0 km 10 km 20 km 30 km 40 km 50 km0 km 10 km 20 km 30 km 40 km 50 km
XX
XX
PVYHT- TULENKÄYTTÖ
XX
OP-TULENKÄYTTÖ
VASTUSTAJA SIDOTAAN
SYVÄÄN TAISTELUUN
ALOITTEELLISEN
PUOLUSTAJAN KANSSA
VASTUSTAJA SIDOTAAN
SYVÄÄN TAISTELUUN
ALOITTEELLISEN
PUOLUSTAJAN KANSSA
KUSTANNUS-
TEHOKKUUS
PAREMPI
TILANNEKUVA
9. 17. lokakuu 2015
8
Kysymyksiä – kommentteja?
• TAISTELUTILAN MUOKKAAMINEN
• VASTUSTAJAN ENNAKOINTI
• JÄRJESTELMÄVAIKUTUS SYVÄLLÄ
ALUEELLA
• JATKUVA KOSKETUS SEKÄ
TULEN JA LIIKKEEN KÄYTTÖ
• NOPEA TAISTELUKYVYN PALAUTUS