significant test
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The document discusses statistical significance tests and the F test. The significant test determines whether data supports or rejects the null hypothesis. The F test compares the variance of two samples and follows an F distribution. It is used to test hypotheses about the equality of variances or means of multiple populations. Assumptions of the F test include random sampling from normal populations with equal but unknown standard deviations and independent samples.
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2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
�
Ca-rich population. Although such an object is too red for any low-
�
cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
�
) with
Λ
CDM. Therefore unlike low-
�
Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
Direct Seeded Rice - Climate Smart Agriculture
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significant test
- 1. Significant Test: • The test which is done for testing the research hypothesis against null hypothesis. • It play a key role in experiments. • It allow researchers to determine whether their data supports or rejects the null hypothesis. • They can accept their null hypothesis.
- 2. F Test • F Test is basically a comparison between test and accepted method. • F stand for a Fisher A famous statistician • By using standard deviation or variance.
- 3. F Test: • Also known as analysis of variance • The test is on base on test statistics which follow F distribution is called F test • In the F test ratio we always take the larger of the two estimates in the numerator and smaller is the denominator • The degree of freedom is n-1
- 4. F Test • If experimental value is less than that of tabulated value then the result will be a significant. • If experimental value is greater than that of tabulated value then the result will not be a significant.
- 7. Uses of F test: • It is used to test hypothesis of equality of two population variances. • It is used to test equality of mean of three or more populations. • It is used for testing the significances of an observed sample multiple question.
- 8. Assumptions: • Samples are drawn at random. • Samples are drawn from the normal populations. • Population standard deviation are treated as equal but unknown. • The samples must be independent