The following forms are from Wappo (Yukian) (Thompson, Park, and Li 2006: 125-7). The first set

The following forms are from Wappo (Yukian) (Thompson, Park,...

Key Concepts

Morphemes

Morphemes are the smallest meaningful units in a language. In analyzing linguistic data, identifying morphemes involves breaking down words into prefixes, roots, suffixes, and other inflectional elements. This process is fundamental to understanding how meaning is constructed in a language.

Allomorphy

Allomorphy describes the phenomenon whereby a single morpheme appears in different phonetic forms without a change in its underlying meaning. Studying allomorphs involves cataloguing these variant forms and noting the conditions under which they occur.

Morphological Segmentation

Morphological segmentation is the process of deconstructing words into their constituent morphemes. This analysis helps in systematic glossing and understanding the structure of words, which is essential in comparative studies and uncovering morphological patterns.

Glossing

Glossing involves providing a word-by-word or morpheme-by-morpheme translation or explanation. This practice is critical for clarifying the function and meaning of each morpheme in a linguistic dataset and for facilitating comparative analysis.

Predictability of Allomorphy

The predictability of allomorphy concerns whether the variation in morpheme forms can be systematically accounted for by phonological rules or morphological environments. When allomorph distribution is predictable, changes in form follow regular patterns; when unpredictable, some variation may be due to lexical idiosyncrasies or historical remnants.

Transcript

00:01 So we have this question of us testing us to test a claim that half of us in that company in the company said that we should replace our password and you know because they said also this question this question this question this question on this cream i'll be like bicycle or this question is telling us to perform an hypothesis test based on the fact that in a usa today survey of 510 people 53 % said that we should replace password with biometric security such as fingerprint the accompanying stats decks display results from a test of the claim that half of us say that we should replay password with biometric security so we are trying to perform an an hypothesis test on the data sets so so we have the sample size to be 510 we have the sample proportion to be 530 so we have the sample proportion to be 53 % that is 0 .53 and we have the population proportion to be 50 that is 0 .5 so the company says half of us are claiming that we want to change our password so we want to change from password to biometric and then so our null hypothesis is going to be on the fact that h0 is p is equal to 0 .5 and our alternative is that a lot of us more than 50 percent of us we want to we want to check change our security system so like we want to change from passwords with biometric security so that simply implies that our p is going to be greater than 0 .5 so because of the fact that we are using an upper we are using this kind of test it simply implies that our test is going to be an upper tailed test an upper tail test or we can also call it a right tailed test either one we use is actually correct so our decision rule our level of significance for this question is 5 % and then at the decision rule states that if your p value is greater than your level of significance you accept your null hypothesis and if your p value is lesser than your level of significance you reject your null hypothesis so our test statistics is going to be a z test and that is because our sample size which is 510 is actually greater than 30 so we have that z is equal to p cup minus p divided by the square root of p times q divided by n where our q is equal to 1 minus p and that gives us 1 minus 0 .5 which gives us 0 .5 so we have z to be equal to 0 .53 minus 0 .5 divided by square root of 0 .5 times 0 .5 divided by 510.

03:17 So 0 .53 minus 0 .0 .03.

03:20 We have 0 .03.

03:23 So i'm going to use my calculator to solve everything around this denominator here.

03:30 So we have 0 .5 times 0 .5 divided by 510.

03:35 Then we have 0 .022 so we have 0 .0 2214 so let us use our calculator to do the match that is 0 .03 divided by 0 .0 2214 we have 1 .36 so our z score is 1 .36 so let us try and sketch uh this z score to to have a glimpse of what it looks like so uh this is what we have here so this is a standard normal distribution curve.

04:12 Zero is at the center.

04:13 So this is 1 .36 here, somewhere around here.

04:16 So we need z greater than 1 .36 and that is this region here.

04:26 So we need to get the probabilistic value, the area under this curve, and that is z greater than 1 .36...

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