Statistic

1 What is a statistic? answer Statistical Data, the information, or the result of applying statistical algorithms on the data.  From the collection of the data, the statistics can be used to infer or describe the Data; This is called descriptive statistics. 2 What is meant by the data?  answer Data is something that does not have meaning for the recipient and are still in need of a treatment.  Data can berujut a state, images, sounds, letters, numbers, math,  language or other symbols that we can use as an ingredient to look at the environment, objects, events, concepts ataupunsuatu. 3 Whether the matches the data is the information? Give an explanation! answer Information is the result of the Data processing systems useful information for its users. so information is the result of the process or result of the processing of data include: combined results, the analysis results, the results of inference,  and the results of computerized information processing system 4 Mention the types of the Data! Give an example!  answer Data Types According to nature 1. Qualitative 1.Berupa labels / names used to identify the attributes of an element 2.Skala measurement: Nominal or Ordinal 3.Data can be numeric or non-numeric ordinal Data No ordinal of the data is basically the result of quantitative and qualitative. Examples of the data is ordinal scaling the individual attitudes. Scaling the individual attitudes toward something can be Tirrenus in various forms, for example: of attitude Strongly agree (5), Agree (4). Neutral (3), Disagree (2) and Strongly disagree (1). At the ordinal level of this the data is didnt have a distance of definitive of data is, for example: Strongly agree (5) and Agree (4) unknown asti distance between the value for the distance between Strongly Agree (5) and Agree (4 ) instead of 1 unit (5 -1). nominal Data Nominal cancel the the data is the data rates According to the level measurement. The nominal value of the data on a single individual has no variation at all, so one individual only has one form of data. Examples of nominal data are: gender, place of residence, year of birth and others. Sexes of data will later be labeled in processing, for example: male and female = 1 = 2. 2. Quantitative 1.Meindikasikan how much (how many / or how much discrete / continuous) 2.Data always numeric 3.Skala measurement: Interval and Ratio. Data Rate Data rate is the highest data rates. Data ratios have the distance between an exact value and has nolai absolute zero is not owned olh of the types of data. Sample Data ratios are: weight, body length, the number of units of objects. If we have 10 balls 10 balls then there was the embodiment of it. When a person has 0 ball then that person does not have any balls. Ratio of the data can be used in mathematical computation, for example: A has 10 balls and B have 8 balls, then A has two more balls than B. Data interval Data interval having a lower level than the data rate. Distance intervals have is the definitive data is the data, but does not have an absolute zero value. Examples of the interval data values ​​are the result of a math test scores. If A and B scored 10 got a score of 8, then certainly A has 2 more value than B.  But there is no absolute zero value, ie when C gets a value of 0, does not mean that the C in math ability is null or empty. Types of Data That Influenced By empirical Characteristics 1. Data parametric A parametric data is called the data is when it meets the following criteria: a. Normally distributed data. D ata having a normal distribution of the data is to represent the population to be studied.  In the visible we can see a histogram of the data is in question is from from, whether or not forming normal curve. b. Homogenity of variance Variations of the the data is in question must be stable without change or homogeneous. c. interval Data The Data is in question is a minimum interval of the data d. Independence Data Obtained data from each indivdu an independent, meaning the response of one individual does not Affect or be affected more individualized response. 2. Non-parametric Non-parametric Stating that the data is the absence of the assumption of a special distribution in a population.  Normal distribution of the data was not met. Non-parametric test results more robust against violations of Assumptions.  Non-parametric test performed if the assumption of free the the data is incomplete, for example, are still in need of the data is on independent random sampling. Time Data Types According to its collection 1. Cross-sectional Data Cross-sectional data is is the the data are collected at Certain times of the same or almost the same. Example: The number of students STEKPI 2005/2006, The number of companies going public in 2006 2. Time Series Data Time Series Data is the the data is collected during the period / period. Example: The movement of the exchange rate in the first month, Rice production in Indonesia in 1997-2006 5 What is the population and sample? Give Example! answer Population is a generalization region consisting of the objects / subjects that have quantity  and Certain characteristics defined by the Researchers to learn and then drawn Conclusions. That is the definition of the population under study. Example Society of residents in a particular area Samples are some of the number and characteristics Possessed by this population,  or a small section of members of the population taken pursuant to the Certain procedures so as to represent the population.  If the population is large, and Researchers may not learn all that is in the population, something like this due to the shortage of funds or fees,  manpower and time, then so Researchers can put on samples taken from the population. Samples will be taken from this population should be rigorously representative or can represent. Example Post-puerperal mothers who are Unable (pain when doing research) 6 Why should a representative sample?  answer Because the sample is representative, the information generated is are are relatively the same as the information contained in the population. So that the Conclusions of the research sample can be applied to the population.  7 What is the function / benefit statistics? Give examples! answer benefits Statistics 1. Obtain an overview of a particular phenomenon with more simple statistical measures. 2. Being Able to draw Conclusions with A Certain level of confidence based on a sample of the population. 3. Can reduce costs through sampling Reviews their reviews. 4. Can the Make the modeling of a problem. 5. Be Able to find out what factors are related to a problem. 6. Be Able to know the effect of a variable 7. Can do forecasting the data is for the future. Example 1. In the area of ​​politics and governance can Predict the roomates will be Elected candidate in the elections through the quick count. 2. In the field of marketing, can see what is affecting sales 3. In the field of finance and Macroeconomics, can know the effect of the government's macro policies on inflation, Increased welfare and others. 4. In the field of medicine and pharmacy, can Determine the effect of a drug to a particular disease. 5. In agriculture, can find superior seeds that can produce higher productivity through the design of experiments. 8 What is a discrete variable and continuous variable? Give examples! answer discrete variable is a variable that can only load sepertangkat limited value or the value of a particular round.  the number of students in a university is a discrete variable Because this amount will be an integer, such as 325; there will be no number of students  325.5 ... Instead of continuous variables are variables you can load a variable set of values ​​that are not limited between two tiers of variable / This continuous variables have the nature of a fractional value, such as a person's height of 1.5 meters, 1.6 meters or 1.75 meters. 9 Mention the statistical methodology! answer understanding of statistical methodology, is solving a problem with statistically consisting of Several stages, the following are the stages of statistical methodology: 1. Identify the problem 2.Pengumpulan of the data or facts 3.Klasifikasi Data 4.Penyajian Data Data analysis

Statistical Data, the information, or the result of applying statistical algorithms on the data.

From the collection of the data, the statistics can be used to infer or describe the Data; This is called descriptive statistics.

Data is something that does not have meaning for the recipient and are still in need of a treatment.

Data can berujut a state, images, sounds, letters, numbers, math,

language or other symbols that we can use as an ingredient to look at the environment, objects, events, concepts ataupunsuatu.

Information is the result of the Data processing systems useful information for its users.

so information is the result of the process or result of the processing of data include: combined results, the analysis results, the results of inference,

and the results of computerized information processing system

Data Types According to nature

1. Qualitative

1.Berupa labels / names used to identify the attributes of an element

2.Skala measurement: Nominal or Ordinal

3.Data can be numeric or non-numeric

ordinal Data

No ordinal of the data is basically the result of quantitative and qualitative.

Examples of the data is ordinal scaling the individual attitudes. Scaling the individual attitudes toward something can be Tirrenus in various forms, for example:

of attitude Strongly agree (5), Agree (4). Neutral (3), Disagree (2) and Strongly disagree (1).

At the ordinal level of this the data is didnt have a distance of definitive of data is, for example: Strongly agree (5) and Agree (4) unknown asti distance between the value for the distance between Strongly Agree (5) and Agree (4 ) instead of 1 unit (5 -1).

nominal Data

Nominal cancel the the data is the data rates According to the level measurement.

The nominal value of the data on a single individual has no variation at all, so one individual only has one form of data.

Examples of nominal data are: gender, place of residence, year of birth and others. Sexes of data will later be labeled in processing, for example: male and female = 1 = 2.

2. Quantitative

1.Mengindikasikan how much (how many / or how much discrete / continuous)

2.Data always numeric

3.Skala measurement: Interval and Ratio.

Data Rate

Data rate is the highest data rates. Data ratios have the distance between an exact value and has nolai absolute zero is not owned olh of the types of data.

Sample Data ratios are: weight, body length, the number of units of objects.

If we have 10 balls 10 balls then there was the embodiment of it. When a person has 0 ball then that person does not have any balls.

Ratio of the data can be used in mathematical computation, for example: A has 10 balls and B have 8 balls, then A has two more balls than B.

Data interval

Data interval having a lower level than the data rate. Distance intervals have is the definitive data is the data, but does not have an absolute zero value.

Examples of the interval data values ​​are the result of a math test scores.

If A and B scored 10 got a score of 8, then certainly A has 2 more value than B.

But there is no absolute zero value, ie when C gets a value of 0, does not mean that the C in math ability is null or empty.

Types of Data That Influenced By empirical Characteristics

1. Data parametric

A parametric data is called the data is when it meets the following criteria:

a. Normally distributed data.

D ata having a normal distribution of the data is to represent the population to be studied.

In the visible we can see a histogram of the data is in question is from from, whether or not forming normal curve.

b. Homogenity of variance

Variations of the the data is in question must be stable without change or homogeneous.

c. interval Data

The Data is in question is a minimum interval of the data

d. Independence

Data Obtained data from each indivdu an independent, meaning the response of one individual does not Affect or be affected more individualized response.

2. Non-parametric

Non-parametric Stating that the data is the absence of the assumption of a special distribution in a population.

Normal distribution of the data was not met. Non-parametric test results more robust against violations of Assumptions.

Non-parametric test performed if the assumption of free the the data is incomplete, for example, are still in need of the data is on independent random sampling.

Time Data Types According to its collection

1. Cross-sectional Data

Cross-sectional data is is the the data are collected at Certain times of the same or almost the same.

Example: The number of students STEKPI 2005/2006,

The number of companies going public in 2006

2. Time Series Data

Time Series Data is the the data is collected during the period / period.

Example: The movement of the exchange rate in the first month,

Rice production in Indonesia in 1997-2006

Population is a generalization region consisting of the objects / subjects that have quantity

and Certain characteristics defined by the Researchers to learn and then drawn Conclusions. That is the definition of the population under study.

Example

Society of residents in a particular area

Samples are some of the number and characteristics Possessed by this population,

or a small section of members of the population taken pursuant to the Certain procedures so as to represent the population.

If the population is large, and Researchers may not learn all that is in the population, something like this due to the shortage of funds or fees,

manpower and time, then so Researchers can put on samples taken from the population. Samples will be taken from this population should be rigorously representative or can represent.

Example

Post-puerperal mothers who are Unable (pain when doing research)

Because the sample is representative, the information generated is are are relatively the same as the information contained in the population. So that the Conclusions of the research sample can be applied to the population.

benefits Statistics

1. Obtain an overview of a particular phenomenon with more simple statistical measures.

2. Being Able to draw Conclusions with A Certain level of confidence based on a sample of the population.

3. Can reduce costs through sampling Reviews their reviews.

4. Can the Make the modeling of a problem.

5. Be Able to find out what factors are related to a problem.

6. Be Able to know the effect of a variable

7. Can do forecasting the data is for the future.

1. In the area of ​​politics and governance can Predict the roomates will be Elected candidate in the elections through the quick count.

2. In the field of marketing, can see what is affecting sales

3. In the field of finance and Macroeconomics, can know the effect of the government's macro policies on inflation, Increased welfare and others.

4. In the field of medicine and pharmacy, can Determine the effect of a drug to a particular disease.

5. In agriculture, can find superior seeds that can produce higher productivity through the design of experiments.

discrete variable is a variable that can only load sepertangkat limited value or the value of a particular round.

the number of students in a university is a discrete variable Because this amount will be an integer, such as 325; there will be no number of students  325.5 ...

Instead of continuous variables are variables you can load a variable set of values ​​that are not limited between two tiers of variable /

This continuous variables have the nature of a fractional value, such as a person's height of 1.5 meters, 1.6 meters or 1.75 meters.

understanding of statistical methodology, is solving a problem with statistically consisting of Several stages, the following are the stages of statistical methodology:

1. Identify the problem

2.Pengumpulan of the data or facts

3.Klasifikasi Data

4.Penyajian Data

Data analysis

Tasks Chapter 2 on the next sheet