Understanding Statistics and Data
Statistics is the science that studies collect, collate, process, analyze, summarize, and present research data. While statistics are processed and the data analysis.
Data (plural) is a description of the object studied. While datum (singular) is a description of the object studied. Data is divided into two, namely Numerical Data (quantity) and data categories (kualitas. numerical data is data in the form of measurements or calculations. While categorical data is data that is not a number. Data was collected:
1. Chopping / count
2. Measure
3. Using tally or pillar
Population and Sample
The population is the entire object that has the characteristics (properties) to be studied together.
The samples are part of the population that can represent the true state of the population studied.
CENTRAL TENDENCY (Size Pooled)
1. Arithmetic mean (Mean)
2. Modus
3. Median
EXAMPLE:
Data: 162.160, 170, 165, 167, 170, 165
Mean => average count
ex: n = 162 +160 +170 +165 +167 +170 +165:7
= 1159:7 = 165.57
Mode => value that often arises
EXAMPLE:
Data: 160, 162, 165, 165, 167, 170, 170
Modus is 165 and 170
Median => middle value after the data sorted
EXAMPLE:
Data sequences: 160, 162, 165, 165, 167, 170, 170
The median is 165
Mean in frequency table
EXAMPLE:
Rated 5 6 7 8 9 10
Frequency 3 6 5 17 5 4
Mean
= E (fx): ef = 3 (5) + 6 (6) + 5 (7) + 17 (8) + 5 (9) + 4 (10): 3 + 6 + 5 + 17 + 5 + 4
= 15 + 36 + 35 + 136 + 45 + 40:40 = 307:40
= 7.675
Size Single Data Transmitting
1. Range (data range)
2. Quartile
3. Reach quartile
4. Reach interquartile
5. Quartile deviation
Range (data range)
Data highest - lowest data
Quartile = Q
Dividers data into 4 parts as much Q1, Q2, Q3 Q2 = Median
Jangakuan quartile = interquartile range
Q3 - Q1
Quartile deviation
Q3 - Q1: 2
Presentation of Data
1. Presenting the data
2. Reading / interpreting data
Presentation of data visualized through:
1. Pictogram / symbol / image
2. Bar chart
3. Diagram line
4. Pie charts
Statistics is the science that studies collect, collate, process, analyze, summarize, and present research data. While statistics are processed and the data analysis.
Data (plural) is a description of the object studied. While datum (singular) is a description of the object studied. Data is divided into two, namely Numerical Data (quantity) and data categories (kualitas. numerical data is data in the form of measurements or calculations. While categorical data is data that is not a number. Data was collected:
1. Chopping / count
2. Measure
3. Using tally or pillar
Population and Sample
The population is the entire object that has the characteristics (properties) to be studied together.
The samples are part of the population that can represent the true state of the population studied.
CENTRAL TENDENCY (Size Pooled)
1. Arithmetic mean (Mean)
2. Modus
3. Median
EXAMPLE:
Data: 162.160, 170, 165, 167, 170, 165
Mean => average count
ex: n = 162 +160 +170 +165 +167 +170 +165:7
= 1159:7 = 165.57
Mode => value that often arises
EXAMPLE:
Data: 160, 162, 165, 165, 167, 170, 170
Modus is 165 and 170
Median => middle value after the data sorted
EXAMPLE:
Data sequences: 160, 162, 165, 165, 167, 170, 170
The median is 165
Mean in frequency table
EXAMPLE:
Rated 5 6 7 8 9 10
Frequency 3 6 5 17 5 4
Mean
= E (fx): ef = 3 (5) + 6 (6) + 5 (7) + 17 (8) + 5 (9) + 4 (10): 3 + 6 + 5 + 17 + 5 + 4
= 15 + 36 + 35 + 136 + 45 + 40:40 = 307:40
= 7.675
Size Single Data Transmitting
1. Range (data range)
2. Quartile
3. Reach quartile
4. Reach interquartile
5. Quartile deviation
Range (data range)
Data highest - lowest data
Quartile = Q
Dividers data into 4 parts as much Q1, Q2, Q3 Q2 = Median
Jangakuan quartile = interquartile range
Q3 - Q1
Quartile deviation
Q3 - Q1: 2
Presentation of Data
1. Presenting the data
2. Reading / interpreting data
Presentation of data visualized through:
1. Pictogram / symbol / image
2. Bar chart
3. Diagram line
4. Pie charts
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