1. A. Statistics is the science of collection, analysis, interpretation or explanation, and presentation of data. It has wide usage in the field of research. In fact all the data collection and interpretation techniques used in Research are part of statistics. It makes use of descriptive statistics for collection of data and inferential statistics for drawing inferences from this set of data.

Statistics is very important in research because that is the backbone of your research. The Numbers gives an easy idea of how you conducted your research. Statistics provides a platform for research as to; How to go about your research, either to consider a sample or the whole population, the Techniques to use in data collection and observation, how to go about the data description (using measure of central tendency).B. Descriptive StatisticsDescriptive statistics provides simple summaries about the sample and about the observations that have been made. Such summaries may be, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation. Mean - the most popular and well known measure of central tendency. It can be used with both discrete and continuous data, although its use is most often with continuous. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set.

Median - the middle score for a set of data that has been arranged in order of magnitude. The median is less affected by outliers and skewed data.

Mode - the most frequent score in our data set. On a histogram it represents the highest bar in a bar chart or histogram. You can, therefore, sometimes consider the mode as being the most popular option.

Frequency Distribution - a description of a variable providing a count of the number of cases that fall into each of the variables categories. It is most commonly presented in a table format and is useful for getting a rough idea of results.

Minimum- the lowest amount or degree recorded

Maximum- the highest amount or degree recorded.

Standard Deviation- a measure of dispersion of a set of data from its mean. The more spread apart the data from the mean, the higher is the deviation

Inferential StatisticsInferential statistics is concerned with making predictions or inferences about a population from observations and analyses of a sample. That is, we can take the results of an analysis using a sample and can generalize it to the larger population that the sample represents. In order to do this, however, it is imperative that the sample is representative of the group to which it is being generalized.>T Test A statistical examination of two population means. A two-sample t-test examines whether two samples are different and is commonly used when the variances of two normal distributions are unknown and when an experiment uses a small sample sizeTypes of t Test Independent Samples t Test (Unpaired t Test) it is use when the observations are not dependent or correlated. Dependent Samples (Paired t Test) it use when the observations are dependent and correlated (e.g. comparing the results of pre-test and post test). >Analysis of Variance (ANOVA) -is a collectionused to analyze the differences between group means and their associated procedures (such as "variation" among and between groups), developedIn the ANOVA setting, the observedin a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides aof whether or notof several groups are equal, and therefore generalizesto more than two groups. Aswould result in an increased chance of committing a statistical, ANOVAs are useful in comparing (testing) three or more means (groups or variables) for.>Least Significant Difference Test and Duncans Multiple Range Test a multiple comparison tests that are used to point out where significant difference lies from among the groups being compared.>Correlation use to evaluate strength of relationship or association between two or more variables considered. Tests of Correlations : Pearson, Kendalls tau-b, Spearman, Chi-square test of independence

2. A. Total sample size: 316 B. Group 1: 154 Group 2: 74 Group 3: 883. Over all Mean: 3.76Verbal Description: The questionnaire has a very good validity indexCorrelations

initial_testFinal_test

initial_testPearson Correlation1.227

Sig. (2-tailed).556

N99

Final_testPearson Correlation.2271

Sig. (2-tailed).556

N99

4.

The correlation coefficient of .227 indicates that the reliability of research instrument is unacceptable.5. A. what is the extent of the level of conformance of LGU personnel to ISO 9001:2008? B.Descriptive Statistics

NMinimumMaximumMeanStd. Deviation

data112.255.003.7136.96049

Valid N (listwise)11

C. This denotes that the extent of the level of conformance of LGU personnel to ISO 9001:2008 is high.

6. What is the socio-demographic profile of the respondents in terms of : a. Length of serviceb. Agelength of service

FrequencyPercentValid PercentCumulative Percent

Valid1-10 years325.030.030.0

11-20 years541.750.080.0

21 years and above216.720.0100.0

Total1083.3100.0

MissingSystem216.7

Total12100.0

age of respondents

FrequencyPercentValid PercentCumulative Percent

Valid10-25 years old325.030.030.0

26-40 years old325.030.060.0

41 and up433.340.0100.0

Total1083.3100.0

MissingSystem216.7

Total12100.0

Statistics

length of serviceage of respondents

NValid1010

Missing22

> The findings presented revealed that in terms of length of service, 3 out 12 or 25% of respondents has 1 10 years of service, 5 out of 12 or 41.7% of the respondents has 11-20 years of service and 2 out of 12 or 16.7% has 21 or more years of service and 2 out of 12 or 16.7% is missing.> In terms of age, the findings revealed that 3 out 12 or 25% of respondents is 10-25 years old, 3 out of 12 or 25% of the respondents 26-40 years old and 4 out of 12 or 33.3% is 41 years old and above. There is 2 out of 12 or 16.7% missing.7. A. Statement of the problem: Is there is a significant increase on the extent of conformance of LGU personnel on ISO 9001: 2008 after the conduct of orientation?Hypothesis: there is an increase on the extent of conformance of LGU personnel on ISO 9001: 2008 after the conduct of orientation.B. Descriptive Statistics

NMinimumMaximumMeanStd. Deviation

initial_assessment52.254.203.1900.92087

Final_assessment52.854.903.9800.78310

Valid N (listwise)5

C. The mean score achieved by the LGU personnel in the initial assessment is 3.19 and 3.98 in the final assessment. This denotes that there is an increase on the extent of conformance of LGU personnel on ISO 9001: 2008 after the conduct of orientation from average to high.8. A. Statement of the problem: Is there is a significant difference on the extent of conformance of LGU personnel to ISO 9001:2008 when grouped according to position status?Hypothesis: Is there is no significant difference on the extent of conformance of LGU personnel to ISO 9001:2008 when grouped according to position statusB. Group Statistics

positionNMeanStd. DeviationStd. Error Mean

conformance1.0053.1900.92087.41183

2.0053.9800.78310.35021

Independent Samples Test

Levene's Test for Equality of Variancest-test for Equality of Means

95% Confidence Interval of the Difference

FSig.tdfSig. (2-tailed)Mean DifferenceStd. Error DifferenceLowerUpper

conformanceEqual variances assumed.596.462-1.4618.182-.79000.54060-2.03663.45663

Equal variances not assumed-1.4617.799.183-.79000.54060-2.04225.46225

C. Significance value .182 denotes that there is no significant difference the extent of conformance of LGU personnel to ISO 9001:2008 when grouped according to position status.

9. A. Statement of the problem: Is the status of employment of LGU personnel correlated with their extent of conformance to ISO 9001:2008? Hypothesis: there is no significant correlation on the status of employment of LGU and their extent of conformance.

B. Correlations

RegularCasual

RegularPearson Correlation1.876

Sig. (2-tailed).052

N55

CasualPearson Correlation.8761

Sig. (2-tailed).052

N55

C. Significant value .876 denotes that there is good correlation on the status of employment of LGU personnel and their extent of conformance to ISO 9001:2008

10. A. statement of the problem: Is the length of service of LGU personnel correlated with their extent of conformance to ISO 9001:2008?Hypothesis: There is no significant correlation on the length of service of LGU personnel and their extent of conformance to ISO 9001:2008.B. Chi-Square Tests

ValuedfAsymp. Sig. (2-sided)

Pearson Chi-Square56.139a6.000

Likelihood Ratio55.1646.000

Linear-by-Linear Association18.8261.000

N of Valid Cases172

a. 2 cells (16.7%) have expected count less than 5. The minimum expected count is 3.58.

C. Significance value is .000 denotes that there is high significant correlation on the length of service of LGU personnel and their extent of conformance to ISO 9001:2008. This implies that the length of service is associated to their extent of conformance to ISO 9001:2008.

11. A. Statement of hypothesis: Is there a significant difference on the extent of conformance of LGU personnel to ISO 9001:2008 when they are grouped according to age?Hypothesis: there is no significant difference on the extent of conformance of LGU personnel to ISO 9001:2008 when they are grouped according to age.B. Between-Subjects Factors

Value LabelN

Age1.0010-25 yrs old5

2.0026-40 yrs old5

3.0041 and up5

Tests of Between-Subjects Effects

Dependent Variable:Adaptation

SourceType III Sum of SquaresdfMean SquareFSig.

Corrected Model5.557a22.7795.349.022

Intercept234.0381234.038450.578.000

Age5.55722.7785.349.022

Error6.23312.519

Total245.82815

Corrected Total11.79014

a. R Squared = .471 (Adjusted R Squared = .383)

Multiple Comparisons

AdaptationLSD

(I) Age(J) AgeMean Difference (I-J)Std. ErrorSig.95% Confidence Interval

Lower BoundUpper Bound

10-25 yrs old26-40 yrs old.7900.45581.109-.20311.7831

41 and up-.7000.45581.151-1.6931.2931

26-40 yrs old10-25 yrs old-.7900.45581.109-1.7831.2031

41 and up-1.4900*.45581.007-2.4831-.4969

41 and up10-25 yrs old.7000.45581.151-.29311.6931

26-40 yrs old1.4900*.45581.007.49692.4831

Based on observed means. The error term is Mean Square(Error) = .519.

*. The mean difference is significant at the 0.05 level.

C. The above table showed that the significant difference lies between age 10-15 years old and above 41 years old and between 26-40 years old and above 41 years old. There is a significant difference observed when comparing ages 10-15 years old, 26-40 years old and 41 years old and above.